Method and system for a posteriori computation of origin-destination matrices relating to gathering of people through analysis of mobile communication network data

ABSTRACT

A method is provided for estimating flows of persons that gathered at an Area of Interest for attending a public happening during a time interval on a day. The Area of Interest is defined by an Area of Interest center and an Area of Interest radius and is covered by a mobile telecommunication network having a plurality of communication stations ( 105   a ) each of which is adapted to manage communications of user equipment in one or more served areas in which the mobile telecommunication network is subdivided.

BACKGROUND OF THE INVENTION Field of the Invention

Traffic analysis is aimed at identifying and predicting variations inthe flow (e.g., people flow, vehicular traffic flow) of physicalentities (e.g., people, land vehicles) moving in a geographic region ofinterest (e.g., a urban area) and over a predetermined observationperiod (e.g., a 24 hours observation period).

A typical, but not limitative, example of traffic analysis isrepresented by the analysis of vehicular (cars, trucks, etc.) trafficflow over the routes of a geographic region of interest, or simplyRegion of Interest (RoI for short). Such analysis allows achieving amore efficient planning of the transportation infrastructure within theRegion of Interest and also it allows predicting how changes in thetransportation infrastructure, such as for example closure of roads,changes in a sequencing of traffic lights, construction of new roads andnew buildings, can impact on the vehicular traffic.

In the following for traffic analysis it is inTended the analysis of themovements of physical entities through a geographic area. Such physicalentities can be vehicles (e.g., cars, trucks, motorcycles, publictransportation buses) and/or individuals.

Since it is based on statistical calculations, traffic analysis needs alarge amount of empirical data to be collected in respect of the Regionof Interest and the selected observation period, in order to provideaccurate results. In order to perform the analysis of traffic, thecollected empirical data are then usually arranged in a plurality ofmatrices, known in the art as Origin-Destination (O-D) matrices. The O-Dmatrices are based upon a partitioning of both the Region of Interestand the observation period.

For partitioning the Region of Interest, the area is subdivided into aplurality of zones, each zone being defined according to severalparameters such as for example, authorities in charge of theadministration of the zones (e.g., a municipality), typology of landlots in the Region of Interest (such as open space, residential,agricultural, commercial or industrial lots) and physical barriers(e.g., rivers) that can hinder traffic (physical barriers can be used aszone boundaries). The size of the zones in which the Region of Interestcan be subdivided, and consequently the number of zones, is proportionalto the level of detail requested for the traffic analysis (i.e., citydistricts level, city level, regional level, state level, etc.).

As well, the observation period can be subdivided into one or more timeslots, each time slot may be defined according to known traffic trends,such as for example peak traffic hours corresponding to when mostcommuters travel to their workplace and/or travel back home. The lengthof the time slots (and thus their number) is proportional to the levelof detail requested for the traffic analysis over the consideredobservation period.

Each entry of a generic O-D matrix comprises the number of physicalentities moving from a first zone (origin) to a second zone(destination) of the area of interest. Each O-D matrix corresponds toone time slot out of the one or more time slots in which the consideredobservation period can be subdivided. In order to obtain a reliabletraffic analysis, sets of O-D matrices should be computed over aplurality of analogous observation periods and should be combined so asto obtain O-D matrices with a higher statistical value. For example,empirical data regarding the movements of physical entities should becollected over a number of (consecutive or not) days (each correspondingto a different observation period), and for each day a corresponding setof O-D matrices should be computed.

O-D matrices that results to be particularly useful are O-D matricesbuild with respect to the formation of “crowds” i.e., a gathering of acertain number of people, gathered in a certain location for, e.g., forattending at public events or happenings, public happenings of the mostdisparate nature, like for example (and non-exhaustively) livetelevision shows, artistic/entertaining performances, culturalexhibitions, theatrical plays, sports contests, concerts, movies,demonstrations and so forth.

In the tasks of urban planning, management of activities (e.g.,transport systems management and emergencies management), and tourismand local marketing, it is useful to have a knowledge of the how andwhen people moved for gathering at certain locations or Areas ofInterests (AoI for short, e.g., a building, such as for example astadium or a theatre or a cinema, the surroundings thereof, a square ora street(s) of a city or town or village, a district etc., generallysmaller than the Region of Interest), e.g. because they attended publichappenings like shows or events (e.g., related to culture, entertaining,politics or sports) that took place within the Area of Interest.

In fact, this knowledge allows building O-D matrices referred to personsthat have attended to public happenings, which allows an insight ofmobility issues and more effective planning of subsequent publichappenings of the same type. Particularly, this knowledge allows a moreeffective planning and managing of resources and activities (such asinfrastructures, transport system and security) directly or indirectlyrelated to similar public happenings that may take place in the future(such as for example sports matches that regularly take place at astadium). Moreover, from a commercial viewpoint, this knowledge allows abetter management of marketing activities inTended to promote similarevents that may take place in the future.

Overview of the Related Art

A typical method for collecting empirical data used to compute O-Dmatrices related to a specific Region of Interest is based on submittingquestionnaires to, or performing interviews with inhabitants of theRegion of Interest and/or to inhabitants of the neighboring areas abouttheir habits in relation to their movements, and/or by installingvehicle count stations along routes of the area of interest for countingthe number of vehicles moving along such routes. The Applicant hasobserved that this method has very high costs and it requires a longtime for collecting a sufficient amount of empirical data. Due to this,O-D matrices used to perform traffic analysis are built seldom, possiblyevery several years, and become out-of-date.

In the art, several alternative solutions have been proposed forcollecting empirical data used to compute O-D matrices.

For example, U.S. Pat. No. 5,402,117 discloses a method for collectingmobility data in which, via a cellular radio communication system,measured values are transmitted from vehicles to a computer. Themeasured values are chosen so that they can be used to determine O-Dmatrices without infringing upon the privacy of the users.

In Chinese PaTent Application No. 102013159 a number plateidentification databased area dynamic origin and destination (OD) dataacquiring method is described. The dynamic OD data is the dynamic originand destination data, wherein O represents origin and D representsdestination. The method comprises the steps of: dividing OD areasaccording to requirements, wherein the minimum time unit is 5 minutes;uniformly processing data of each intersection in the area every 15minutes by a traffic control center; detecting number plate data;packing the number plate identification data; uploading the number plateidentification data to the traffic control center; comparing a platenumber with an identity (ID) number passing through the intersections;acquiring the time of each vehicle passing through each intersection;acquiring the number of each intersection in the path through which eachvehicle passes from the O point to the D point by taking the platenumber as a clue; sequencing the intersections according to timesequence and according to the number of the vehicles which pass throughbetween the nodes calculating a dynamic OD data matrix.

WO 2007/031370 relates to a method for automatically acquiring trafficinquiry data, e.g. in the form of an O-D matrix, especially as inputinformation for traffic control systems. The traffic inquiry data arecollected by means of radio devices placed along the available routes.

Nowadays, mobile phones have reached a thorough diffusion among thepopulation of many countries, and mobile phone owners almost alwayscarry their mobile phone with them. Since mobile phones communicate witha plurality of base stations of the mobile phone networks, and each basestation operates over a predetermined geographic area (or cell) which isknown to the mobile phone network, mobile phones result to be optimalcandidates as tracking devices for collecting data useful for performingtraffic analysis. For example, N. Caceres, J. Wideberg, and F. Benitez“Deriving origin destination data from a mobile phone network”,Intelligent Transport Systems, IET, vol. 1, no. 1, pp. 15-26, 2007,describes a mobility analysis simulation of moving vehicles along ahighway covered by a plurality of GSM network cells. In the simulationthe entries of O-D matrices are determined by identifying the GSM cellsused by the mobile phones in the moving vehicles for establishing voicecalls or sending SMS.

US 2006/0293046 proposes a method for exploiting data from a wirelesstelephony network to support traffic analysis. Data related to wirelessnetwork users are extracted from the wireless network to determine thelocation of a mobile station. Additional location records for the mobilestation can be used to characterize the movement of the mobile station:its speed, its route, its point of origin and destination, and itsprimary and secondary transportation analysis zones. Aggregating dataassociated with multiple mobile stations allows characterizing andpredicting traffic parameters, including traffic speeds and volumesalong routes.

In F. Calabrese, F. Pereira, G. Di Lorenzo, L. Liu, C. Ratti, “Thegeography of taste: analyzing cell-phone mobility and social events”, inProceedings of the 8th International Conference, Pervasive Computing2010, Helsinki, Finland, May 17-20, 2010, Lecture Notes in ComputerScience Volume 6030, 2010, pp 22-37, discloses an analysis of crowdmobility during special events. Nearly 1 million cell-phone traces havebeen analyzed and their destinations have been associated with socialevents. It is observed that the origins of people attending at an eventare strongly correlated to the type of event, with implications in citymanagement, since the knowledge of additive flows can be a criticalinformation on which to take decisions about events management andcongestion mitigation.

SUMMARY OF THE INVENTION

The Applicant has observed that, generally, methods and systems known inthe art provide unsatisfactory results, as they are not able todetermine (or have a limited ability in determining) whether a UE ownerhas moved to/from an Area of Interest (AoI) where one or more publichappenings have been held, for attending thereat or for other reasons(for example, because the UE owner resides or has a business inproximity of, or within, the area of interest). In addition, the resultsprovided by the known solutions are strongly influenced by the size ofthe Area of Interest selected for identifying the movements andsubsequently computing O-D matrices related to one or more publichappenings. In other words, if the area of interest has a large size,the movement of a certain number of UE owners that have not attended atthe one or more public happenings will be taken into account in thecomputing of O-D matrices of movements related to such one or morepublic happenings. Conversely, if the area of interest has small size,the movements of a certain number of UE owners that attended at the oneor more public happenings will be excluded from the computing of the O-Dmatrices of movements related to such one or more public happenings.

Therefore, subsequent planning and managing of resources and activities(of the type mentioned above) based on O-D matrices obtained by themethods and systems known in the art will achieve a limited efficiencydue to the limited accuracy thereof.

The Applicant has thus coped with the problem of devising a system andmethod adapted to overcome the problems affecting the prior artsolutions.

The Applicant has designed an adaptive method for identifying themovements of UE owners that attended at the one or more publichappenings and for computing O-D matrices related to such movements.

The Applicant has found that it is possible to determine the size of anoptimal area of interest on the basis of operational information relatedto UE during the course of the one or more public happening and in acertain number of days preceding the one or more public happenings.

Particularly, one aspect of the present invention proposes a method ofestimating flows of persons that gathered at an Area of Interest forattending a public happening Sn during a time interval [Tsn, Ten] on aday gn. Said Area of Interest is defined by an Area of Interest center Cand an Area of Interest radius Ra and is covered by a mobiletelecommunication network having a plurality of communication stationseach of which is adapted to manage communications of user equipment inone or more served areas in which the mobile telecommunication networkis subdivided. The method comprises the following steps: a) defining aplurality of calculated radius values Rk of the Area of Interest radiusRa, and, for each calculated radius value Rk: b) identifying a firstnumber Unk of user equipment associated with at least one event recorder_(v) of a corresponding event e_(v) of interaction occurred betweenthe user equipment and the mobile communication network during the timeinterval [Tsn, Ten] on the day gn within the Area of Interest; c)identifying a second number Upnk of user equipment associated with atleast one event record er_(v)′ of a corresponding event e_(v)′ ofinteraction occurred between the user equipment and the mobilecommunication network during the time interval [Tsn, Ten] for each dayof a predetermined number P of previous days gpn preceding the day gnwithin the Area of Interest; d) combining the first number Unk of userequipment and the second numbers Upnk of user equipment for obtaining astatistical quantity Znk; e) detecting the occurrence of the publichappening Sn if the statistical quantity Znk reaches a certain thresholdZth; f) computing an optimum radius value Ro of the Area of Interestradius Ra as the average of the calculated radius values Rk within whichthe public happening Sn is detected; g) identifying persons thatgathered for attending at the public happening Sn within an Area ofInterest having the Area of Interest radius Ra equal to the optimumradius value Ro during the time interval [Tsn, Ten] on the day gn withinthe Area of Interest based on a first time fraction f1 indicating aprobability that the user equipment has been in the Area of Interestduring the time interval [Tsn, Ten] on the day gn and on a second timefraction f2 indicating a probability that the user equipment has been inthe Area of Interest during the previous days gpn for each userequipment identified at step b), h) computing at least one matrixaccounting for movements of persons identified at step g) within aRegion of Interest comprising the Area of Interest to the Area ofInterest during at least one observation time period OsPn comprising thetime interval [Tsn, Ten], and i) computing at least one matrixaccounting for movements of persons identified at step g) within aRegion of Interest comprising the Area of Interest from the Area ofInterest during at least one observation time period DsPn comprising thetime interval [Tsn, Ten].

Preferred features of the present invention are set forth in thedependent claims.

In one embodiment of the present invention, the public happening Sncomprises a plurality of public happenings Sn, and the method furthercomprises the step of: j) iterating steps b) to e) for each one of thepublic happenings Sn of the plurality of public happenings Sn, andwherein the step f) of computing an optimum radius value Ro of the Areaof Interest radius Ra as the average of the computed radius values Rkwithin which the public happening is detected, comprises: computing anoptimum radius value Ro of the Area of Interest radius Ra as the averageof the computed radius values Rk weighted by a number Dsk of detectedpublic happenings Sn within the Area of Interest having the Area ofInterest radius Ra equal to the same computed radius values Rk, saidnumber Dsk of detected public happenings Sn being the sum of the publichappenings Sn determined by iterating step e).

In one embodiment of the present invention, the steps h) of computing atleast one matrix accounting for movements of persons identified at stepg) within a Region of Interest comprising the Area of Interest to theArea of Interest during at least one observation time period TPn, OsPn,DsPn comprising the time interval [Tsn, Ten], and i) of computing atleast one matrix accounting for movements of persons identified at stepg) within a Region of Interest comprising the Area of Interest from theArea of Interest during at least one observation time period TPn, OsPn,DsPn comprising the time interval [Tsn, Ten], comprise: k) subdividingthe Region of Interest into at least two zones z_(q); l) associating afirst zone zn_(h) of the at least two zones zn with the Area ofInterest, the first zone zn_(h) comprising at least partially the Areaof Interest; m) subdividing the at least one time period TPn, OsPn, DsPninto one or more time slots ts_(m,n), ts_(m′,n), ts_(m″,n); n)identifying a number of persons that moved from a second zone zn₀ of theat least two zones z_(q) to the first zone zn_(h) comprising at leastpartially the Area of Interest during each one of the one or more timeslots ts_(m′,n) of the at least one observation time period OsPn, and o)identifying a number of persons that moved to the second zone zn_(I−1)of the at least two zones z_(q) from the first zone zn_(h) comprising atleast partially the Area of Interest during each one of the one or moretime slots ts_(m″,n) of the at least one observation time period DsPn.

In one embodiment of the present invention, the step n) of identifying anumber of persons that moved from a second zone zn₀ of the at least twozones z_(q) to the first zone zn_(h) comprising at least partially theArea of Interest during each one of the one or more time slots ts_(m)′of the at least one observation time period OsPn, comprises: identifyingas the second zone zn₀ the zone z_(q) of the Region of Interest thatcomprises a previous position pn₀ associated with a previous eventrecord er₀ of a corresponding previous event e₀ of interaction occurredbetween the user equipment and the mobile communication network duringthe observation time period OsPn on the day gn within the Region ofinterest, the previous event record er₀ being recorded before a firstevent record er_(f) of a corresponding first event e_(f) of interactionoccurred between the user equipment and the mobile communication networkduring the time interval [Tsn, Ten] on the day gn within the Area ofInterest.

In one embodiment of the present invention, the step n) of identifying anumber of persons that moved from a second zone zn₀ of the at least twozones z_(q) to the first zone zn_(h) comprising at least partially theArea of Interest during each one of the one or more time slots ts_(m)′of the at least one observation time period OsPn, further comprises: p)computing an origin matrix for each one of the one or more time slotsts_(m′,n) in which the at least one time period OsPn has beensubdivided, each entry od(0,h)_(n) of the origin matrix being indicativeof the number of persons that, during the corresponding time slot, movedto the first zone zn_(h) comprising at least partially the Area ofInterest from the second zone zn₀ of the at least two zones z_(q).

In one embodiment of the present invention, the step p) of computing anorigin matrix for each one of the one or more time slots ts_(m′,n) inwhich the at least one time period OsPn has been subdivided, each entryod(0,h)_(n) of the origin matrix being indicative of the number ofpersons that, during the corresponding time slot, moved to the firstzone zn_(h) comprising at least partially the Area of Interest from thesecond zone zn₀ of the at least two zones z_(q), comprises: increasing avalue of the entry od(0,h)_(n) indicative of persons that moved from thesecond zone zn₀ of the at least two zones z_(q) to the first zone zn_(h)comprising at least partially the Area of Interest of the origin matrixassociated with a time slot ts_(m)′ comprising a previous time data tdn₀associated with the previous event record er₀.

In one embodiment of the present invention, the step p) of computing anorigin matrix for each one of the one or more time slots ts_(m′,n) inwhich the at least one time period OsPn has been subdivided, each entryod(0,h)_(n) of the origin matrix being indicative of the number ofpersons that, during the corresponding time slot ts_(m′,n) moved to thefirst zone zn_(h) comprising at least partially the Area of Interestfrom the second zone zn₀ of the at least two zones z_(q), comprises:increasing a value of the entry od(0,h)_(n) indicative of persons thatmoved from the second zone zn₀ of the at least two z_(q) zones to thefirst zone zn_(h) comprising at least partially the Area of Interest ofthe origin matrix associated with a time slot ts_(m′,n) comprising afirst time data tdn_(f) associated with the first event record er_(f).

In one embodiment of the present invention, the step p) of computing anorigin matrix for each one of the one or more time slots ts_(m′,n) inwhich the at least one time period OsPn has been subdivided, each entryod(0,h)_(n) of the origin matrix being indicative of the number ofpersons that, during the corresponding time slot ts_(m′,n), moved to thefirst zone zn_(h) comprising at least partially the Area of Interestfrom the second zone zn₀ of the at least two zones z_(q), comprises:identifying an origin movement time interval [tdn₀, tdn_(f)] delimitedby a previous time data tdn₀ associated with the previous event recorder₀ of the corresponding previous event e₀ and a first time data tdn_(f)associated with the first event record er_(f) of the corresponding firstevent e_(f), and increasing a value of the entry od(0,h)_(n) indicativeof persons that moved from the second zone zn₀ of the at least two zonesz_(q) to the first zone zn_(h) comprising at least partially the Area ofInterest of the origin matrices associated with time slots ts_(m′,n)comprised at least partially in the movement time interval [tdn₀,tdn_(f)].

In one embodiment of the present invention, the step o) of identifying anumber of persons that moved to the second zone zn_(I+1) of the at leasttwo zones z_(q) from the first zone zn_(h) comprising at least partiallythe Area of Interest during each one of the one or more time slotsts_(m″,n) of the at least one observation time period DsPn, comprises:identifying as the second zone zn_(I+1) the zone z_(q) of the Region ofInterest that comprises a next position pn_(I+1) associated with a nextevent er_(I+1) record of a corresponding previous event e_(I+1) ofinteraction occurred between the user equipment and the mobilecommunication network during the observation time period DsPn on the daygn within the Region of interest, the next event record er_(I−1) beingrecorded after a last event record er_(I) of a corresponding last evente_(I) of interaction occurred between the user equipment and the mobilecommunication network during the time interval DsPn on the day gn withinthe Area of Interest.

In one embodiment of the present invention, step o) of identifying anumber of persons that moved to the second zone zn_(I+1) of the at leasttwo zones z_(q) from the first zone zn_(h) comprising at least partiallythe Area of Interest during each one of the one or more time slotsts_(m″,n) of the at least one observation time period DsPn, furthercomprises: q) computing a destination matrix for each one of the one ormore time slots ts_(m″,n) in which the at least one time period DsPn hasbeen subdivided, each entry od(h,I+1)_(n) of the destination matrixbeing indicative of the number of persons that, during the correspondingtime slot ts_(m″,n), moved from the first zone zn_(h) comprising atleast partially the Area of Interest to the second zone zn_(I+1) of theat least two zones z_(q).

In one embodiment of the present invention, the step q) of computing adestination matrix for each one of the one or more time slots ts_(m″,n)in which the at least one time period DsPn has been subdivided, eachentry od(h,I+1)_(n) of the destination matrix being indicative of thenumber of persons that, during the corresponding time slot ts_(m″,n),moved from the first zone zn_(h) comprising at least partially the Areaof Interest to the second zone zn_(I+1) of the at least two zones z_(q),comprises: increasing a value of the entry od(h,I+1)_(n) indicative ofpersons that moved to a second zone zn_(I+1) of the at least two zonesz_(q) from the first zone zn_(h) comprising at least partially the Areaof Interest of the destination matrix associated with a time slotts_(m″,n) comprising a last time data tdn_(I) associated with the lastevent record er_(I).

In one embodiment of the present invention, the step q) computing adestination matrix for each one of the one or more time slots ts_(m″,n)in which the at least one time period DsPn has been subdivided, eachentry od(h,I+1)_(n) of the destination matrix being indicative of thenumber of persons that, during the corresponding time slot ts_(m″,n),moved from the first zone zn_(h) comprising at least partially the Areaof Interest to the second zone zn_(I+1) of the at least two zones z_(q),comprises: increasing a value of the entry od(h,I+1)_(n) indicative ofpersons that moved to a second zone zn_(I+1) of the at least two zonesz_(q) from the first zone zn_(h) comprising at least partially the Areaof Interest of the destination matrix associated with a time slotts_(m″,n) comprising a next time data tdn_(I+1) associated with the nextevent record er_(I−1).

In one embodiment of the present invention, the step q) computing adestination matrix for each one of the one or more time slots ts_(m″,n)in which the at least one time period DsPn has been subdivided, eachentry od(h,I+1)_(n) of the destination matrix being indicative of thenumber of persons that, during the corresponding time slot ts_(m″,n),moved from the first zone zn_(h) comprising at least partially the Areaof Interest to the second zone zn_(I+1) of the at least two zones z_(q),comprises: identifying a destination movement time interval [tdn_(I),tdn_(I+1)] delimited by a last time data tdn_(I) associated with thelast event record er_(I) of the corresponding last event e_(I) and anext time data tdn_(I+1) associated with the next event record er_(I+1)of the corresponding next event e_(I+1), and increasing a value of theentry od(h,I+1)_(n) indicative of persons that moved to a second zonezn_(I+1) of the at least two zones z_(q) from the first zone zn_(h)comprising at least partially the Area of Interest of the destinationmatrices associated with time slots ts_(m″,n) at least partiallycomprised in the destination movement time interval [tdn_(I),tdn_(I+1)].

In one embodiment of the present invention, the step g) identifyingpersons that gathered for attending at the public happening Sn within anArea of Interest having the Area of Interest radius Ra equal to theoptimum radius value Ro during the time interval [Tsn, Ten] on the daygn within the Area of Interest based on a first time fraction f1indicating a probability that the user equipment has been in the Area ofInterest during the time interval [Tsn, Ten] on the day gn and on asecond time fraction f2 indicating a probability that the user equipmenthas been in the Area of Interest during the previous days gpn for eachuser equipment identified at step b), comprises: estimating aprobability pu that each person is attending at the public happening Sn,for each person associated with a respective user equipment of the firstnumber identified at step c), based on the first time fraction f1 andthe second time fraction f2.

In one embodiment of the present invention, the step n) of identifying anumber of persons that moved from a second zone zn₀ of the at least twozones z_(q) to the first zone zn_(h) comprising at least partially theArea of Interest during each one of the one or more time slots ts_(m′,n)of the at least one observation time period OsPn, further comprises:computing the number as a combination of the probabilities pu of eachperson that moved from a second zone zn₀ of the at least two zones z_(q)to the first zone zn_(h) comprising at least partially the Area ofInterest during each one of the one or more time slots ts_(m′,n) of theat least one observation time period OsPn has attended at the publichappening Sn.

In one embodiment of the present invention, the step o) of identifying anumber of persons that moved to the second zone zn_(I+1) of the at leasttwo zones z_(q) from the first zone zn_(h) comprising at least partiallythe Area of Interest during each one of the one or more time slotsts_(m″,n) of the at least one observation time period DsPn, furthercomprises: computing the number as a combination of the probabilities puof each person that moved to the second zone zn_(I−1) of the at leasttwo zones z_(q) from the first zone zn_(h) comprising at least partiallythe Area of Interest during each one of the one or more time slotsts_(m″,n) of the at least one observation time period DsPn has attendedat the public happening.

In one embodiment of the present invention, step k) of subdividing theRegion of Interest into at least two zones zn, comprises: subdividingthe Region of Interest into a plurality of zones z_(q), and wherein thestep n) of identifying a number of persons that moved from a second zonezn₀ of the at least two zones z_(q) to the first zone zn_(h) comprisingat least partially the Area of Interest during each one of the one ormore time slots ts_(m′,n) of the at least one observation time periodOsPn, further comprises: identifying a number of persons that moved fromeach zone z_(q) of the plurality of zones to the first zone zn_(h)comprising at least partially the Area of Interest during each one ofthe one or more time slots ts_(m′,n) of the at least one observationtime period OsPn, and wherein the step o) of o) of identifying a numberof persons that moved to the second zone zn_(I+1) of the at least twozones z_(q) from the first zone zn_(h) comprising at least partially theArea of Interest during each one of the one or more time slots ts_(m″,n)of the at least one observation time period DsPn, comprises: identifyinga number of persons that moved to each zone z_(q) of the plurality ofzones from the first zone zn_(h) comprising at least partially the Areaof Interest during each one of the one or more time slots ts_(m″,n) ofthe at least one observation time period DsPn.

In one embodiment of the present invention, the method further comprisesthe step r) of iterating steps l) to q) for each one of the publichappenings Sn.

Another aspect according to the present invention proposes a systemcoupled with a wireless telecommunication network for estimating flowsof persons that gathered at an Area of Interest, the system comprising:a computation engine adapted to process data retrieved from a mobiletelephony network; a repository adapted to store data regardinginteractions between the user equipment and the mobile telephonynetwork, computation results generated by the computation engine and,possibly, any processing data generated by and/or provided to thesystem, and an administrator interface operable for modifying parametersand/or algorithms used by the computation engine and/or accessing datastored in the repository. The system further comprises a memory elementstoring a software program product configured for implementing themethod above through the system.

In one embodiment of the present invention, the system further comprisesat least one user interface adapted to receive inputs from, and toprovide output to a user of the system, the user comprising one or morehuman beings and/or one or more external computing systems subscriber ofthe services provided by the system.

BRIEF DESCRIPTION OF THE DRAWINGS

These and others features and advantages of the solution according tothe present invention will be better understood by reading the followingdetailed description of an embodiment thereof, provided merely by way ofnon-limitative example, to be read in conjunction with the attacheddrawings, wherein:

FIG. 1 is a schematic representation of a system for identifyingmovements and for computing O-D matrices related to one or more publichappenings according to an embodiment of the present invention;

FIGS. 2A-2E are exemplary shapes in which the cells of the mobilecommunication network may be modeled according to an embodiment of thepresent invention;

FIGS. 3A-3E are exemplary shapes that the AoI to be determined may takeaccording to an embodiment of the present invention;

FIGS. 4A-4D are relevant cells among the cells of the mobilecommunication network with respect to the AoI according to an embodimentof the invention, and

FIG. 5 is a schematic view of a geographic region of interest forperforming a traffic analysis of physical entities (e.g., vehicles), thegeographic area of interest being subdivided into a plurality of zones;

FIG. 6 shows a generic O-D matrix related to the geographic region ofinterest RoI of FIG. 5, corresponding to a certain time slot of anobservation period; FIG. 7 shows a generic O-D sub-matrix obtained fromthe O-D matrix of FIG. 6, denoted to as Origin matrix, which comprisesthe movements of UE owners from zones of the RoI to the AoI;

FIG. 8 shows a generic O-D sub-matrix obtained from the O-D matrix ofFIG. 6, denoted to as Destination matrix, which comprises the movementsof UE owners from the AoI to different zones of the RoI;

FIG. 9 shows a set of O-D matrices, of the type shown in FIG. 6, eachreferred to a respective one of a plurality of time slots making up theobservation period, and used for performing a traffic analysis;

FIG. 10 shows a set of Origin matrices, of the type shown in FIG. 7,corresponding to a respective plurality of time slots making up theobservation period, and used for performing the traffic analysis;

FIG. 11 shows a set of Destination matrices, of the type shown in FIG.8, corresponding to a respective plurality of time slots making up theobservation period, and used for performing the traffic analysis;

FIGS. 12A-12G are a schematic flowchart of an algorithm for identifyingmovements and O-D matrices computation referred to one or more publichappenings according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The Applicant has designed an adaptive method for identifying themovements (or flows) of User Equipments, UE in the following (e.g. amobile phone, a smartphone, a tablet with 2G-3G-4G connectivity, etc.),that attended at one or more public happenings and for computing O-Dmatrices related to such movements.

The method according to the present invention allows identifying anoptimal area of each considered public happening that is used asstarting point for identifying persons who attended the public happeningand for identifying their movement before and/or after having attendedthe public happening.

Such an optimal area will depend on the place where each publichappening has been held (e.g., a stadium, a square, a theatre etc.) butalso on the kind of public happening (e.g., sport match, concert,theatrical play etc.).

In order to correctly identifying the movements of the persons that haveattended at each public happening, and excluding persons that live orthat usually spend time in the neighborhood of the place where thepublic happening is held (which could be identified and their movementstracked as well by slightly modifying the method according to thepresent invention), an “influence area” or Area of Interest for eachpublic happening has to be determined first.

For example, let it be assumed that the place where each publichappening has been held is a circumference featuring a center and aradius, firstly it is needed to determine an optimal value for theradius, and then it is possible to determine a set of served areas of awireless communication network influenced by each one of the one or morepublic happenings, i.e. which served areas have received the (phone)data traffic associated with the UE owned by persons that attended eachpublic happening.

For example, let it be considered a stadium in which two sport matchesare held: the first sport match gathers 10000 attendees, while thesecond sport match, being more interesting, gathers 30000 attendees.During the second match the (phone) data traffic (where ‘data’ isreferred to any information exchanged between the UE and a mobilecommunication network serving it, such as for example voice traffic,SMS/MMS traffic, Internet traffic, operating data exchanged for managingcommunications etc.) will be greater than in the first sport match, and,in order to fulfill all the (phone) data traffic a greater number ofserved areas are needed with respect to the served areas involved duringthe first sport match. Therefore the area of the second sport match ispresumably greater than the area of the first sport match. The methodaccording to the present invention is able to identify the served areasof the wireless network effectively involved in serving the UEs owned bythe attendees in both the first and second sport matches.

The method according to the present invention described in the followingallows a posteriori identifying the movements, e.g. represented in theform of the so-called Origin Destination matrices, of those persons thathave attended one or more public happenings held at a given place. Thesame method according to an embodiment of the invention may be usedwithout the need of substantial changes for identifying movements ofpersons that visited a predetermined Point of Interest (e.g., a museum)during different days or during different hours.

The method according to the present invention described in thefollowing, exploits information of UEs owned by the persons thatattended the one or more public happenings, such as for example CDR(Call Detail Record) and/or VLR (Visitor Location Register), which areknown to the provider of the wireless communication network which servesthe UE. Such information mainly comprises position-time pairs associatedto any interaction between the UE and the wireless communication network(the position substantially corresponding to the served area of thewireless communication network where the UE is located at the moment ofthe interaction).

In the following reference will be made substantially only to a wirelesscommunication network for telephony services and to the CDR, even thoughany position-time pair information may be exploited by the methodaccording to the present invention without requiring any substantialchange. For example, the method may exploit information provided by‘short range’ wireless communication networks, such as for exampleWireless Local Area Networks (WLAN), Bluetooth-based networks and/orother network capable of providing position information such as forexample the Global Positioning System (GPS).

With reference to the drawings, FIG. 1 is a schematic representation ofa system for identifying movements and for computing O-D matricesrelated to one or more public happenings, simply denoted as system 100hereinafter, according to an exemplary embodiment of the presentinvention.

The system 100 allows performing an estimation of movements, and thusallows computing corresponding O-D matrices, of people that attended atone or more public happenings, of the most disparate nature, like forexample (and non-exhaustively) live television shows,artistic/entertaining performances, cultural exhibitions, theatricalplays, sports contests, concerts, movies, demonstrations and so forth.

The system 100 is coupled to a mobile communication network 105, such asa (2G, 3G, 4G or higher generation) mobile telephony network, and isconfigured for receiving from the mobile communication network 105positioning data of each UE of individuals located in a geographic Areaof Interest, AoI in brief, schematized in FIG. 1 as the area within thedash-and-dot line 107 (e.g., a building, such as for example a stadiumor a theatre or a cinema, the surroundings thereof, a square or astreet(s) of a city or town or village, a district etc.).

The AoI 107 (further described in the following) may generally comprisea core place (e.g., a stadium, a theater, a city square and so on) whereone or more public happenings have taken place and, possibly,surroundings (e.g., nearby parking lots, nearby streets, nearbytransport stations and so forth) of the core place.

The mobile communication network 105 comprises a plurality of (two ormore) communication stations 105 a (e.g., radio base stations of themobile telephony network) geographically distributed through the AoI107. Each communication station 105 a is adapted to managecommunications of UE (not shown, such as for example mobile phones) inone or more served areas or cells 105 b (in the example at issue, threecells are served by each communication station 105 a) as will bediscussed in greater detail below.

Even more generally, each communication station 105 a of the mobilecommunication network 105 is adapted to interact with any UE locatedwithin one of the cells 105 b served by such communication station 105 a(e.g., interactions at power on/off, at location area update, atincoming/outgoing calls, at sending/receiving SMS and/or MMS, atInternet access etc.). Such interactions between UE and mobilecommunication network 105 will be generally denoted as events e_(v)(v=1, . . . , V; where V is an integer) in the following.

The system 100 comprises a computation engine 110 configured to beadapted to process data retrieved from the mobile communication network105, and a repository 115 (such as a database, a file system, etc.)configured to be adapted to store data regarding interactions betweenthe UE and the mobile communication network 105, computation resultsgenerated by the computation engine 110 and, possibly, any processingdata generated by and/or provided to the system 100 (generally in abinary format). The system 100 is provided with an administratorinterface 120 (e.g., a computer) configured and operable for modifyingparameters and/or algorithms used by the computation engine 110 and/oraccessing data stored in the repository 115.

Preferably, the system 100 comprises one or more user interfaces 125(e.g., a user terminal, a software running on a remote terminalconnected to the system 100) adapted to receive inputs from, and toprovide output to a user of the system 100. The term “user of thesystem” as used in the present disclosure may refer to one or more humanbeings and/or to external computing systems (such as a computer network,not shown) of a third party being subscriber of the services provided bythe system 100 and enabled to access the system 100 e.g., undersubscription of a contract with a service provider owner of the system100, and typically with reduced right of access to the system 100compared to the right of access held by an administrator of the system100 operating through the administrator interface 120.

It should be appreciated that the system 100 may be implemented in anyknown manner; for example, the system 100 may comprise a singlecomputer, or a network of distributed computers, either of physical type(e.g., with one or more main machines implementing the computationengine 110 and the repository 115, connected to other machinesimplementing administrator and user interfaces 120 and 125) or ofvirtual type (e.g., by implementing one or more virtual machines in acomputer network).

The system 100 is adapted to retrieve (and/or receive) an event recorder_(v) for each event e_(v) occurred between a UE and the mobilecommunication network 105 (through one of its communication stations 105a) within the AoI 107. Preferably, each event record er_(v) retrieved bythe system 100 from the mobile communication network 105 comprises in anon-limitative manner an identifier of the UE that is involved in thecorresponding event e_(v) (e.g., the UE identifier may be selected asone or more among the International Mobile Equipment Identity—IMEI, theInternational Mobile Subscriber Identity—IMSI and the Mobile SubscriberISDN Number—MSISDN code), time data (also denoted as timestamps)indicating the time at which the corresponding event e_(v) has occurred,and UE geographical position data, e.g. spatial indications based on thecell 105 b in which the UE is located at the time of occurrence of thecorresponding event e_(v).

In one embodiment of the present invention, the UE identifier of the UEinvolved in the event record er_(v) may be provided as encryptedinformation in order to ensure the privacy of the UE owner. Anyway, ifthe need arises, the encrypted information (i.e., the identity of theowner of the UE corresponding to the UE identifier) may be decrypted byimplementing a suitable decryption algorithm, such as for example thealgorithm SHA256 described in “Secure Hash Standard (SHS)”, NationalInstitute of Standards and Technology FIPS—180-4, Mar. 6, 2012.

The system 100 may retrieve (and/or receive) the event records er_(v)related to a generic UE from the mobile communication network 105 byacquiring records of data generated and used in the mobile communicationnetwork 105. For example, in case the mobile communication network 105is a GSM network, Charging Data Records (CDR), also known as call datarecords, and/or Visitor Location Records (VLR) may be retrieved from themobile communication network 105 and re-used as event records er_(v).The CDR is a data record (usually used for billing purposes by a mobiletelephony service provider operating through the mobile communicationnetwork 105) that contains attributes specific to a single instance of aphone call or other communication transaction performed between a UE andthe mobile communication network 105. The VLR are databases listing UEthat have roamed into the jurisdiction of a Mobile Switching Center(MSC, not shown) of the mobile communication network 105, which is amanagement element of the mobile communication network 105 managingevents over a plurality of communication stations 105 a. Eachcommunication station 105 a in the mobile communication network 105 isusually associated with a respective VLR.

Conversely, if the mobile communication network 105 is a LTE network,records of data associated with the event records er_(v) of a generic UEare generated by a Mobility Management Entity, or MME, comprised in themobile communication network 105, which is responsible for a UE trackingand paging procedure in LTE networks (where no VLR is implemented).

It should be noted that the method described in the present disclosuremay be implemented by using any source of data (e.g., provided by one ormore WiFi networks) from which it is possible to obtain event recordser_(v) comprising a univocal identifier of individuals (such as the UEidentifier mentioned above), a position indication of such individuals,and a time indication of an instant during which such event hasoccurred.

In operation, event records er_(v) may be continuously retrieved by thesystem 100 from the mobile communication network 105. Alternatively,event records er_(v) may be collected by the system 100 periodically,e.g. for a predetermined time period (e.g., every certain number ofhours, on a daily or weekly basis). For example, event records er_(v)may be transferred from the mobile communication network 105 to thesystem 100 as they are generated, in a sort of “push” modality, or eventrecords er_(v) may be collected daily in the mobile communicationnetwork 105 and then packed and transferred to the system 100periodically or upon request by the system 100.

The event records er_(v) retrieved from the mobile communication network105 are stored in the repository 115, where they are made available tothe computation engine 110 for processing. Preferably, event recordser_(v) generated by a same UE are grouped together in the repository115, i.e. event records er_(v) are grouped together if they comprise acommon UE identifier and are denoted to as event records group erg_(l)(e.g., l=0, . . . , L, L≥0) hereinafter.

Preferably, the computation engine 110 processes an algorithm foridentifying and analyzing the traffic flows of people (described in thefollowing) implemented by a software program product stored in a memoryelement 110 a of the system 110, comprised in the computation engine 110in the example of FIG. 1, even though the software program product couldbe stored in the repository 115 as well (or in any other memory elementprovided in the system 100).

Even more preferably, the event records er_(v) are processed accordingto (as discussed in detail below) instructions provided by the systemadministrator (through the administrator interface 120), for examplestored in the repository 115, and, possibly, according to instructionsprovided by a user (through the user interface 125) Finally, thecomputation engine 110 provides the results of the processing performedon the event records er_(v) to the user through the user interface 125,and optionally stores such processing results in the repository 115.

Turning now to FIGS. 2A-2E, they are exemplary shapes in which the cells105 b of the mobile communication network 105 may be modeled accordingto an embodiment of the present invention.

For the purposes of the present invention, each cell 105 b of the mobilecommunication network 105 may be modeled as an area (as shown in FIG.2A) having a respective cell center B (not necessarily corresponding toa geographic position of the communication station 105 a) and arespective cell radius Rc, that encloses an effectively served area (notshown) served by the corresponding communication station 105 a (e.g., anarea in which each point is reached by radio-signals transmitted by thecommunication station 105 a). Alternatively, the cell radius Rc maycorrespond to the radius of a circumference that encloses a substantialpart of the effectively served area, such as the 85% or more of theeffectively served area, such as for example the 90%, of the effectivelyserved area.

It should be noted that the cells 105 b are not limited to a disc-likeshape, in facts, the cells 105 b may have the shape of a, preferablyregular, polygon. In this case, the cell center B corresponds to acenter of mass (or centroid) of the polygon, while the cell radius Rccorresponds to a segment adjoining the center of mass of the polygon,i.e. the cell center B, with a vertex of the polygon (as shown in FIGS.2B and 2D) or with a midpoint of a side of the polygon (as shown inFIGS. 2C and 2E).

The effectively served area, and therefore the cell radius Rc, may bedefined by means of well-known network planning software tools used by aprovider of the mobile communication network 105, or may be computed onthe basis of (omnidirectional or directional, such as with 120°radiation angles) anTennas radiation diagrams and simple radiationmodels such as for example the ones described in Theodore S. Rappaport,“Wireless Communications”, Prentice Hall, 1996.

Alternatively, the mobile communication network 105 may be modeled bymeans of a Voronoi tessellation diagram, in which each Voronoi cellcorresponds to a cell 105 b of the mobile communication network 105(since Voronoi tessellation diagrams are well known in the art, they arenot discussed further herein).

Preferably, the modeling, the list and the number of cells 105 b of themobile communication network 105 are inputted to the system 100 by theadministrator through the administrator interface 120.

In the solution according to an embodiment of the present invention, thesystem 100 is adapted to identify whether individuals attended to one ormore public happenings occurred within the AoI 107 based on events egenerated by an interaction between the UE and the mobile communicationnetwork 105 serving such UE within the AoI 107.

Turning now to FIGS. 3A-3E, they are exemplary shapes that the AoI 107to be determined may take according to an embodiment of the presentinvention.

Generally, the AoI 107 for one or more public happenings may be modeledas an area having an AoI center C and an AoI radius Ra. For example, theAoI 107 may be delimited by a circumference centered in the AoI center Cand having the AoI radius Ra as circumference radius (as shown in FIG.3A).

It should be noted that the AoI 107 may have shapes different from thecircumference. For example, the AoI 107 may have the shape of a,preferably regular, polygon. In this case, the AoI center C correspondsto a center of mass (or centroid) of the polygon, while the AoI radiusRa corresponds to a segment adjoining the center of mass of the polygonwith a vertex of the polygon (as shown in FIGS. 3B and 3D) or with amidpoint of a side of the polygon (as shown in FIGS. 3C and 3E) in asimilar way as for the cells 105 b modeling discussed above.

The AoI center C may be set (e.g., by a user through the user interface125 or by a system administrator through the administrator interface120) as a (geographical) central point of the AoI 107 (e.g., ageographical central point of the core place), as an address of the coreplace of the one or more public happenings, as a point provided by amapping software, such as web mapping services (e.g., Google Maps™,OpenStreetMap™, etc.).

As will be described in more detail in the following, the AoI radius Ramay take zero or negative value along with positive values. In case theAoI radius Ra takes zero or negative value, the AoI 107 is limited tothe AoI center C (i.e., the core place of the one or more publichappenings). The meaning of zero or negative values for the AoI radiusRa will be further clarified by reference to such zero or negativevalues in the embodiments described below.

The algorithm described in the following is configured to determine anoptimum radius value Ro for the AoI radius Ra of the AoI 107. In oneembodiment of the invention, the optimum radius value Ro is determinedby means of iterative steps starting from a minimum radius value Rmin toa maximum radius value Rmax (as described hereinbelow). Preferably, theminimum radius value Rmin and the maximum radius value Rmax are set bythe administrator of the system 100 through the administrator interface120.

In an embodiment of the present invention, on the basis of statisticalanalysis of empirical data regarding a plurality of past publichappenings the minimum radius value Rmin is set equal to −1500 m(Rmin=−1500 m), while the maximum radius value Rmax is set equal to 1500m (Rmax=1500 m).

Having defined the shape of the cell 105 b of the mobile communicationnetwork 105 and the shape of the AoI 107, the concept of relevant cell,i.e., a cell 105 b of the mobile communication network 105 that isconsidered at least partially belonging to the AoI 107 according to anembodiment of the invention will be now be introduced.

FIGS. 4A-4D are relevant cells 405 a-d among the cells 105 b of themobile communication network 105 with respect to the AoI 107 accordingto an embodiment of the invention.

In one embodiment of the invention, given the AoI 107 having the AoIcenter C and the generic cell 105 b having the cell center B and thecell radius Rc, the generic cell 105 b may be considered a relevant cell405 a-d for the AoI 107 if the following inequality is verified:Dist(C,B)≤|Rc+Ra|,  (1)where Dist(C, B) is the geographical distance between the AoI center Cand the cell center B.

According to the value of the AoI radius Ra of the AoI 107, inequality(1) may take three different meanings.

Namely, if the AoI radius Ra of the AoI 107 is greater than zero (i.e.,Ra>0), inequality (1) reduces to:Dist(C,B)≤(Rc+Ra),  (2)and the generic cell 105 b is considered a relevant cell (such as thecase of relevant cell 405 a in FIG. 4A) for the AoI 107 having an AoIradius Ra greater than zero if the area of the AoI 107 and the genericcell 105 b are at least partially superimposed (even if the AoI center Cfall outside the generic cell 105 b).

If the AoI radius Ra of the AoI 107 is equal to zero (i.e., Ra=0) theinequality (1) reduces to:Dist(C,B)≤Rc,  (3)and the generic cell 105 b is considered a relevant cell (such as thecase of relevant cells 405 b and 405 c in FIGS. 4B and 4C) for the AoI107 having an AoI radius Ra equal to zero if the AoI center C of the AoI107 is comprised in the generic cell 105 b.

Finally, if the AoI radius Ra of the AoI 107 is smaller than zero (i.e.,Ra<0) the generic cell 105 b is considered a relevant cell (such as thecase of relevant cell 405 d in FIG. 4D) for the AoI 107 having an AoIradius Ra smaller than zero if the AoI center C of the AoI 107 iscomprised within the generic cell 105 b at a distance from the cellcenter B equal to or smaller than Rc−|Ra|.

A generic public happening S, apart from being held at a specificlocation (i.e., the AoI 107), has a start time Ts and an end time Te.Consequently, for the purposes of the present invention the genericpublic happening S has a relevant duration equal to an observation timeinterval [Ts, Te] (i.e., a time interval that starts at the start timeTs and ends at the end time Te, lasting for Te−Ts time units, e.g.seconds, minutes or hours).

Both the start time Ts and the end time Te may be defined so as tocorrespond to the official (officially announced) start and end timesscheduled for that generic public happening S. Nevertheless, theApplicant has observed that by anticipating the start time Ts withrespect to the official start time of the generic public happening S itis possible to take into account the fact that people (i.e., UE ownersthat attend at the generic public happening S) arrive at the AoI 107before the official start time of the generic public happening S, whichmay be useful for collecting data about a trend in time of a flow ofattendees arriving at the generic public happening S. For example, onthe basis of empirical data of previous public happenings, the Applicanthas found that the start time Ts may be usefully anticipated to 60minutes before the official start time of the generic public happening Sin order to take into account the trend of attendees arriving at thegeneric public happening S.

Similarly, the Applicant has observed that the end time Te may bedelayed with respect to the official end time of the generic publichappening S in order to take into account the fact that people leave theAoI 107 after the official end time of the generic public happening S,which may be useful for collecting data about a trend in time of a flowof attendees leaving the generic public happening S. For example, on thebasis of empirical data of previous public happenings, the Applicant hasfound that the end time Ts may be usefully delayed by 30 minutes afterthe official end time of the generic public happening S in order to takeinto account the trend of attendees leaving the generic public happeningS.

Anyway, the administrator through the administrator interface 120,and/or the user through the user interface 125, may set any custom starttime Ts and end time Te for the generic public happening S. For example,the start time Ts and the end time Te may be set in order to define theobservation time interval [Ts, Te] shorter than the effective durationof the generic public happening S (i.e., shorter than the duration ofthe whole public happening) in order to analyze a number or a variationof persons in the crowd that attended at the generic public happening Sonly during a sub-portion of the whole time duration of the genericpublic happening S.

FIG. 5 is a schematic view of a geographic Region of Interest 500, inthe following simply denoted as RoI 500, which is a different entitywith respect to the AoI 107 defined above and is not to be mistaken withthe latter.

The RoI 500 is a selected geographic region within which a trafficanalysis is performed in order to compute O-D matrices according to anembodiment of the present invention. For example, the RoI 500 may beeither a district, a town, a city, or any other kind of geographic area.Moreover, the RoI 500 may comprise a number of sub-regions havingnon-adjacent geographical locations, such as for example a plurality ofdifferent cities, different counties and/or different nations (and soon).

It should be noted that the RoI 500 size and exTent is not limited bythe AoI 107 size and/or geographical location. Indeed, the RoI 500 maycomprise a whole city in which the AoI 107 is located and/or a set oflocations (such as for example airports, bus/train stations, etc.)within a selected range from the AoI 107 from which it is probable thatflows of people towards/from the AoI 107 originate/are directed.Nevertheless, the RoI 500 may comprise a set of one or more nations fromwhich people that attended at the one or more shows probably live (e.g.,in case of public happenings of international relevance).

Let be assumed, as non-limiting example, that a traffic analysis (e.g.,an analysis of people flow) over the RoI 500 is performed in order toidentifying movements of people that attended at the one or more showsheld in the AoI 107 and for computing O-D matrices referred to themovements of people identified by the traffic analysis.

The RoI 500 is delimited by a boundary, or external cordon 505. The RoI500 is subdivided into a plurality of traffic analysis zones, or simplyzones z_(q) (q=1, . . . , Q; where Q is an integer number, and Q>0) inwhich it is desired to analyze traffic flows. In the example shown inFIG. 5, the RoI 500 is subdivided into nine zones z₁, . . . , z₉ (i.e.,Q=9).

Each zone z_(q) may be advantageously determined by using the alreadydescribed zoning technique. According to this technique, each zone z_(q)may be delimited by administrative (city limits, National boundaries,etc.) and/or physical barriers (such as rivers, railroads etc.) withinthe RoI 500 that may hinder the traffic flow and may comprise adjacentlots of a same kind (such as open space, residential, agricultural,commercial or industrial lots) which are expected to experience similartraffic flows. It should be noted that the zones z_(q) may differ insize one another. Generally, each zone z_(q) is modeled as if alltraffic flows starting or ending therein were concentrated in arespective single point or centroid 510 q (i.e., 510 ₁, . . . , 510 ₉).In other words, the centroid 510 q of the generic zone z_(q) representsan ideal hub from or at which any traffic flow starts or ends,respectively.

Anyway, it is pointed out that the solution according to embodiments ofthe present invention is independent from the criteria used to partitionthe RoI 500 into zones.

Considering now FIG. 6, an O-D matrix 600 corresponding to the RoI 500is depicted. The O-D matrix 600 is referred to a respective timeinterval or time slot is of an observation time period TP, as describedin greater detail in the following. It should be noted that theobservation time period is generally different (e.g., greater than) fromthe observation time interval [Ts, Te] of the public happenings.

The generic O-D matrix 600 is typically a square matrix having Q rows iand Q columns j. Each row and each column are associated with acorresponding zone z_(q) of the RoI 500; thus, in the example of FIG. 1,the O-D matrix 600 comprises nine rows i=1, . . . , 9 and nine columnsj=1, . . . , 9.

Each row i represents an origin zone z_(i) for traffic flows of movingphysical entities (for example land vehicles) while each column jrepresents a destination zone z_(j) for traffic flows of such movingphysical entities. In other words, each generic element or entry od(i,j)of the O-D matrix 600 represents the number of traffic flows starting inthe zone z_(i) (origin zone) and ending in the zone z_(j) (destinationzone) in the corresponding time slot.

The main diagonal of the O-D matrix 600, which comprises the entriesod(i,j) having i=j (i.e., entries od(i,j) having the same zone z_(i)both as origin and destination zone), is usually left empty (e.g., withvalues set to 0) or the values of the main diagonal entries od(i,j) arediscarded since they do not depict a movement between zones of theregion of interest (i.e., such entries do not depict a flow of people).

In the following it will be assumed, for the sake of simplicity andconciseness, that the AoI 107 is comprised within a happening zone z_(h)(wherein 1≤h≤Q) of the zones z_(q) in which the RoI 500 is subdivided.It should be noted that the AoI 107 does not necessarily correspond withthe happening zone z_(h) by which it is comprised (i.e., the AoI 107 maybe smaller than the happening zone z_(h)).

It should further be noted that, without departing from the scope of theinvention, the AoI 107 may also exTend at least partially over more thanone zone z_(q) of the RoI 500 (for example in case of big publichappenings, such as for example city festivals, it is possible toidentify a plurality of happening zones within the RoI 500).

From the generic O-D matrix 600 it is possible to extract an O-Dsub-matrix, denoted as Origin Matrix (OM), or O matrix 700 in thefollowing, containing only the movements of people towards the AoI 107(i.e., ended at the happening zone z_(h) that encloses the AoI 107). Itshould be noted that the O matrix 700 reduces to a vector in case theAoI 107 is comprised within a single happening zone z_(h) of the RoI500.

For example in FIG. 7 it is shown an O-D sub-matrix 700 that containsthe entries od(i,j) referred to movements from any one of the zonesz_(q) of the RoI 500 towards (i.e., that ended at) the happening zonez_(h) within the time slot of the observation time period to which thegeneric O-D matrix 600 is referred.

Similarly, from the generic O-D matrix 600 it is possible to extractanother O-D sub-matrix, denoted as Destination Matrix (DM), or D matrix800 in the following, containing only the movements of people away fromthe AoI 107 (i.e., starting at the happening zone z_(h) that enclosesthe AoI 107). It should be noted that the D matrix 800 reduces to avector in case the AoI 107 is comprised within a single happening zonez_(h) of the RoI 500.

For example in FIG. 8 it is shown an O-D sub-matrix 800 that containsthe entries od(i,j) referred to movements from the happening zone z_(h)towards (i.e., that ended at) any one of the zones z_(q) of the RoI 500within the time slot of the observation time period to which the genericO-D matrix 600 is referred.

As outlined above, in order to obtain a more detailed and reliabletraffic analysis, a predetermined observation period TP of the trafficflows in the region of interest is also established and it is subdividedinto one or more (preferably a plurality) of time slots ts_(m) (m=1, . .. , M, where M is an integer number, and M>0). Each time slot ts_(m)ranges from an initial instant t_(0(m)) to a next instant t_(0(m+1))(excluded) which is the initial instant of the next time slot ts_(m+1),or:ts _(m) −[t _(0(m)) ,t _(0(m+1))).

Anyway, embodiments of the present invention featuring overlapping timeslots are not excluded. Also, the time slots ts_(m) into which theobservation period is subdivided may have different lengths from oneanother.

For each one of the time slots ts_(m) a respective O-D matrix 600 m iscomputed that accounts for the movements that have taken place duringthe time slot ts_(m). Therefore, a sequence or O-D set 900 of M O-Dmatrices 600 m, as shown in FIG. 9, is obtained that providesinformation of movements of people from/to each one of the differentzones z_(q) of the RoI 500.

The computing of the O-D matrices 600 m is now described.

For any pair of consecutive event records er_(v) and er_(v+1) of a sameUE recorded at two subsequent event times t_(k) and t_(k−1) within theobservation period TP and at two distinct locations p_(k) and p_(k+1)each one associated with a respective zones z_(i) and z_(j), a movementof the UE owner is identified.

If both the event times t_(k) and t_(k+1) belong to a same time slotts_(m) of the observation period the entry od(i,j) of the O-D matrix 600m of the set of O-D matrices associated with the time slot ts_(m) isincreased by one unit, i.e. the movement is associated with the O-Dmatrix 600 m.

Conversely, if the event times t_(k) and t_(k+1) belong to differenttime slots ts_(m) and ts_(m+1) substantially three possible options areavailable for assigning values to the entries od(i,j) in computing theO-D matrices 600 m:

1. the entry od(i,j) of the O-D matrix 600 m, of the set 900 of O-Dmatrices, associated with the time slot ts_(m) is increased by one unit,i.e. the movement is associated with the O-D matrix 600 m;

2. the entry od(i,j) of the O-D matrix 600 m+1, of the set 900 of O-Dmatrices, associated with the time slot ts_(m+1) is increased by oneunit, i.e. the movement is associated with the O-D matrix 600 m+1 or

3. the movement may be distributed between each one of the matrices 600m-600 m+x of the set 900 associated with each one of the time slotsts_(m)−ts_(m−x) (where x≥1) comprising at least partially a movementtime interval [t_(k), t_(k+1)] defined by the event times t_(k) andt_(k+1). (i.e., the event time t_(k) occurring during the time slotts_(m) and the event time t_(k+1) occurring during the time slotts_(m+x)). Preferably, the movement is associated in a proportionalmanner to each one of the time slots ts_(m)−ts_(m+x), and consequentlyassigned to the corresponding matrices 600 m-600 m+x, according to a(time) portion of the movement time interval [t_(k), t_(k+1)] havingtaken place during each one of the time slots ts_(m)−ts_(m+x).

For example, considering two consecutive time slots ts_(m) and ts_(m+1)comprising the movement time interval [t_(k), t_(k+1)], a first (time)portion of the movement time interval [t_(k), t_(k+1)], e.g. 60% of[t_(k), t_(k−1)], falls in the time slot ts_(m) while the second (time)portion of the movement time interval [t_(k), t_(k−1)], e.g. theremaining 40% of [t_(k), t_(k+1)], falls in the other time slotts_(m+1), the entry od(i,j) of the O-D matrix 600 m is increased by 0.6,while the entry od(i,j) of the other O-D matrix 600 m+1 is increased by0.4.

The first option privileges the initial time (t_(k)) at which a movementis started, the second option privileges instead the final time(t_(k+1)) of the movement, while the third option considers the durationof the time interval.

Preferably, the administrator of the system 100 through theadministrator interface 120 and/or the user of the system 100 throughthe user interface 125 may choose among the three options according totheir needs.

Therefore, an origin set, or O set, of a plurality of O matrices 700 maybe extracted from the O-D set 900 of O-D matrices 600, i.e. one O matrix700 for each one of the M O-D matrices 600 of the O-D set 900.Similarly, a destination set, or D set, of a plurality of D matrices 800may be extracted from the O-D set 900 of O-D matrices 600, i.e. one Dmatrix 800 for each one of the M O-D matrices 600 of the O-D set 900.

More preferably, in the embodiment of the present invention, theobservation time period TP of the traffic analysis may be subdividedinto two (possibly at least partially overlapped) observationsub-periods, namely an origin observation sub-period OsP and adestination observation sub-period DsP.

The origin observation sub-period OsP is a sub-period of the observationtime period TP preferably starting before the start time Ts of thegeneric public happening S and useful for the analysis of people movingtowards the AoI 107 as described in the following.

For example, given the observation time interval [Ts, Te], the originobservation sub-period OsP precedes the start time Ts of the observationtime interval [Ts, Te] by an origin time range Ho and ends at the endtime Te of the observation time interval [Ts, Te] (i.e., OsP=[Ts−Ho,Te]).

The destination observation sub-period DsP is a sub-period of theobservation time period TP preferably starting at the start time Ts ofthe generic public happening S and useful for the analysis of peopleleaving the AoI 107 as described in the following.

For example, given the observation time interval [Ts, Te], thedestination observation sub-period DsP starts at the start time Ts ofthe observation time interval [Ts, Te] and ends after a destination timerange Hd following the end time Te of the observation time interval [Ts,Te] (i.e., DsP=[Ts, Te+Hd]).

Advantageously, the origin time range Ho and the destination time rangeHd are set according to the relevance of the generic public happening Swith which are associated. For example, for a generic public happeningof local relevance the origin time range Ho and the destination timerange Hd may be set to respective values (generally different one fromthe other) comprised between 2 to 6 hours, while in case of a genericpublic happening having an international relevance the origin time rangeHo and the destination time range Hd may exceed 24 hours.

In addition, the origin time range Ho and the destination time range Hdand/or the origin observation sub-period OsP and the destinationobservation sub-period DsP may be adjusted by the administrator of thesystem 100 through the administrator interface 120 and/or by the userthrough the user interface 125 according to any specific requirements.

Each one of the observation sub-period OsP and the destinationobservation sub-period DsP may comprise a respective number of timeslots ts_(m) of the observation period TP.

In the considered example, the origin observation sub-period OsP of theobservation period TP comprises six (origin) time slots ts_(m′) (e.g.,1≤m′≤6=M′). Advantageously, each time slot ts_(m′) has a respectivelength that is inversely proportional to an expected people flowinTensity (i.e. proportional to a number of expected movements towardsthe AoI 107 in that time slot ts_(m′) e.g., the expected traffic densitymay be based on previous traffic analysis or estimation). For example,time slots having low expected traffic inTensity (i.e., time slotsfarthest from the observation time interval [Ts, Te]), may be set to be2 hours long, time slots having mid expected traffic inTensity may beset to be 1 hours long and time slots having high expected trafficinTensity (i.e., time slots closest to the observation time interval[Ts, Te]) may be set to be 0.5 hours long.

Therefore, by considering the origin observation sub-period OsP forwhich the origin time range Ho is, e.g., 5 hours long (Ho=5 hr), theorigin observation sub-period OsP comprises six time slots ts_(m)′having the following structure: ts₁=[Ts−5, Ts−3), ts₂=[Ts−3, Ts−2),ts₃=[Ts−2, Ts−1), ts₄=[Ts−1, Ts−0.5), ts₅=[Ts−0.5, Ts] and ts₆=[Ts, Te].The last time slot ts₆ of the origin observation sub-period OsPcorresponds to the observation time interval [Ts, Te] selected for thegeneric public happening S and takes into account for movements takingplace during the observation time interval [Ts, Te], which may be largerthan the official duration of the generic public happening S asdescribed above.

Similarly, in the considered example, the destination sub-period DsP ofthe observation period TP comprises three (destination) time slotsts_(m″) (e.g., 1≤m″≤3=M″). As in the previous case, each time slotts_(m″) has a respective length that is inversely proportional to anexpected people flow inTensity in that time slot ts_(m).

Therefore, by considering the destination observation sub-period DsP forwhich the destination time range Hd is, e.g., 3 hours long (Hd=3 hr),the destination observation sub-period DsP comprises three time slotsts_(m)″ having the following structure: ts₁=[Ts, Te), ts₂=[Te, Te+1),ts₃=[Te+1, Te+3). The first time slot ts₁ of the destination observationsub-period OsP corresponds to the observation time interval [Ts, Te]selected for the generic public happening S and takes into account formovements taking place during the observation time interval [Ts, Te],which may be larger than the official duration of the generic publichappening S.

Anyway, it is pointed out that the solution according to embodiments ofthe present invention is independent from criteria applied forpartitioning the observation period into time slots.

It should be noted that generally the number of (origin) time slotsts_(m′) and (destination) time slots ts_(m″) of the origin observationsub-period OsP and of the destination observation sub-period DsP,respectively, may differ one from the other (as in the example ofabove), may overlap completely or partially.

Preferably, the number and time exTent of the time slots ts_(m″)comprised into the origin observation sub-period OsP and the time slotsts_(m″) comprised into the destination observation sub-period DsP may beadjusted by the administrator of the system 100 through theadministrator interface 120 and/or by the user through the userinterface 125 according to any specific requirements.

Considering FIG. 10, it shows a set 1000 of O matrix 700 m′ of the typeof the O matrices 700 described with respect to FIG. 7 referred to theRoI 500 and to a generic public happening S held in the zone z_(h),wherein any one of the O matrices 700 m′ of the set 1000 is calculatedfor a corresponding (origin) time slot ts_(m′) of the plurality of timeslots comprised into the origin observation sub-period OsP.

In the considered example, the set 1000 of O matrices 700 m′, comprisessix O matrices 700 ₁-700 ₆, each one referred to a corresponding onetime slot ts₁-ts₆ comprised in the origin observation sub-period OsP.

The set 1000 of O matrices 700 m′ accounts for the flows of peopletowards (i.e., movements ended at) the AoI 107 during the originobservation sub-period OsP which is the observation period within whichpeople that attended at the show gathered at the AoI 107.

Considering FIG. 11, it shows a set 1100 of D matrices 800 m″ of thetype of the D matrices 800 described with respect to FIG. 8 referred tothe RoI 500 and to a generic public happening S held in the zone z_(h),wherein any one of the D matrices 800 m″ of the set 1100 is calculatedfor a corresponding (destination) time slot ts_(m″) comprised into thedestination observation sub-period DsP.

The set 1100 of D matrices 800 m″ accounts for the flows of peopleleaving (i.e., movements started from) the AoI 107 during thedestination observation sub-period DsP which is the observation periodwithin which people that attended at the show left the AoI 107.

Having described the system 100, time (i.e., the start time Ts and theend time Te) and spatial (i.e., the AoI center C and AoI radius Ra ofthe AoI 107) characteristics of a generic public happening S, thestructure of a set 900 of O-D matrices 600 m of the movements related tothe generic public happening S, and the set 1000 of O matrices 700 m′and the set 1100 of D matrices 800 m″ derived from the O-D matrices 600m, an algorithm for identifying and analyzing the traffic flows ofpeople that attended the one or more public happenings Sn (i.e., foridentifying and analyzing movements and computing O matrices and Dmatrices (or O-D matrices) related to people that attended one or morepublic happenings Sn) according to an embodiment of the presentinvention will be now described, by making reference to FIGS. 12A-12G,which are a schematic block diagram thereof.

Let N (where N is an integer number, that may be defined by theadministrator through the administrator interface 120 and/or by the userthrough the user interface 125) be a number of public happenings Sn,where n is a happening variable indicating which of the N publichappenings is considered (i.e., 1≤n≤N), held in a same AoI 107 and ofwhich the movements within the RoI 500 of people that attended to one ormore of the public happenings Sn (i.e., the traffic flow of only thosepeople that have actually attended one or more of the public happeningsSn) is to be analyzed.

For each public happening Sn, an observation day gn during which thepublic happening Sn has been held, the start time Tsn and the end timeTen are defined. It should be noted that the start time Tsn and the endtime Ten may vary from one public happening Sn to the other.

Moreover, for each public happening Sn a set of previous days gpn (where1≤p≤P and P is an integer number) preceding the observation day gn areconsidered. The number P of previous days gpn considered is preferablyset by the administrator (through the administrator interface 120). Inan embodiment of the present invention, the administrator sets thenumber P of previous days gpn according to the storage capabilities ofthe repository 115 (i.e., in order to be able to store all the dataregarding the P previous days gpn) and/or on the basis of computationalcapabilities of the computation engine 110 (i.e., in order to be able toprocess all the data regarding the P previous days gpn). Preferably, theadministrator sets the number P of previous days gpn also on the basisof a statistical analysis of past public happenings of the same kind(i.e., cultural, entertaining, politics or sport shows).

The Applicant has found that setting the number P of previous days gpnequal to 6 (i.e., P=6) provides good results for most kind of publichappenings (although this should not be construed as limitative for thepresent invention).

A first portion of the algorithm for identifying and analyzing trafficflows of people that attended the one or more public happenings Sn isconfigured to determine the optimum radius value Ro for the AoI radiusRa of the AoI 107 on the basis of the data regarding all the N publichappenings Sn considered.

Initially (step 1202) the AoI center C, the observation days gn and thestart times Tsn and end times Ten are inputted to the system 100, e.g.by a user through the user interface 125 or by the administrator throughthe administrator interface 120.

Afterwards (step 1204), an iteration variable k is initialized to zero(i.e., k=0), a detected number of happening variable DSk is initializedto zero as well (i.e., DSk=0) and a calculated radius value Rk isinitially set to the minimum radius value Rmin (i.e., Rk=Rmin). Theiteration variable k accounts for the number of iterations of the firstportion of the algorithm, the detected number of happening variable DSkaccounts for the number of public happenings Sn detected during theiterations of the first portions of the algorithm (as described in thefollowing) and the calculated radius value Rk is used in determining theoptimum radius value Ro.

Next (step 1206), the relevant cells 405 a-d for the AoI 107 having aAoI radius Ra equal to the calculated radius value Rk (Ra=Rk) areidentified by means of the inequality (1) as described above.

Afterwards (step 1208), the day variable n is initialized, e.g. to unity(n=1).

All the event records er_(v) referred to the observation day gn duringan observation time interval [Tsn, Ten] and referred to the relevantcell 405 a-d determined at step 1206 are retrieved (step 1210) from therepository 115.

Subsequently (step 1212), a first UE number Unk is computed as thenumber of UEs corresponding to (i.e., being associated with) at leastone event record er_(v) among the event records er_(v) referred torelevant cells 405 a-d that have been retrieved at previous step 1206 iscomputed (the first UE number Unk depends on the relevant cells and,therefore, on the calculated radius value Rk).

Similarly, all the event records er_(v), referred to the previous daysgpn during the observation time interval [Tsn, Ten] and having takenplace within the relevant cell 405 a-d determined at step 1206 areretrieved (step 1214) from the repository 115.

Then (step 1216), it is computed a second UE number Upnk for each one ofthe previous days gpn as the number of UEs corresponding to at least oneevent record er_(v′) among the event records er_(v′) referred torelevant cells 405 a-d that have been retrieved at previous step 1206(the second UE numbers Upnk depends on the relevant cells and,therefore, on the calculated radius value Rk).

The second UE numbers Upnk just computed are combined (step 1218) inorder to determine an average UE number μnk (with

$\left. {{\mu\;{nk}} = {\sum\limits_{p = 1}^{P}\;{Upnk}}} \right)$and a UE number standard deviation σnk (with

$\left. {{\sigma\;{nk}} = \sqrt{\frac{\sum\limits_{p = 1}^{P}\;\left( {{Upnk} - {\mu\;{nk}}} \right)^{2}}{P}}} \right)$of the UE number within the relevant cells 405 a-d during theobservation time interval [Tsn, Ten] on the P previous days gpnconsidered.

The average UE number μnk and the UE number standard deviation σnk arecombined (step 1220) with the first UE number Unk in order to obtain a(statistical) quantity defined z-score Znk (which depends on thecalculated radius value Rk):Znk=(Unk−μnk)/σnk.  (4)

The z-score Znk just computed is compared (step 1222) with a z-scorethreshold Zth and it is checked whether the z-score Znk is greater thanthe z-score threshold Zth, or:Znk>Zth.  (5)

The z-score threshold Zth is a value preferably defined by theadministrator through the administrator interface 120 on the basis ofstatistical analysis of past public happenings of the same kind (e.g.,cultural, entertaining, politics or sport happenings).

The Applicant has found that setting the z-score threshold Zth equal to2 (i.e., Zth=2) provides good results for most kind of public happenings(although this should not construed as limitative for the presentinvention).

In the affirmative case (exit branch Y of decision block 1222), i.e. thez-score Znk is greater than the z-score threshold Zth (i.e., Znk>Zth),one of the N public happenings Sn is detected and the detected number ofhappenings variable DSk is increased by unity (step 1224; i.e.,DSk=DSk+1) and operation proceeds at step 1226 (described hereinbelow).

In the negative case (exit branch N of decision block 1222), i.e. thez-score Znk is equal to, or lower than, the z-score threshold Zth (i.e.,Znk≤Zth), the day variable n is increased by unity (step 1226; i.e.,n=n+1).

Then (step 1228), it is checked whether the happening variable n islower than, or equal to, the number N of public happening Sn:n≤N.  (6)

In the affirmative case (exit branch Y of decision block 1228), i.e. thevariable n is lower than, or equal to, the number N of overall publichappenings Sn (n≤N), operation returns to step 1210 for analyzing theevent records er_(v) referred to the public happening Sn held on thenext observation day gn.

In the negative case (exit branch N of decision block 1228), i.e. thehappening variable n is greater than the number N of overall publichappenings Sn (n>N; i.e., all the N public happenings Sn have beenanalyzed), the variable k is increased by unity (step 1230; i.e., k=k+1)and the calculated radius value Rk is increased (step 1232):Rk=Rmin+kΔ,  (7)

where Δ is an iteration width that may be defined by the administrator(e.g., Δ=100 m), thus each calculated radius value Rk is separated fromthe next calculated radius value by an iteration width Δ. It should benoted that the iteration width Δ defines a maximum iteration value kmaxfor the iteration variable k—and, therefore, a maximum number ofiterations for determining the optimum radius value Ro—as:kmax=(|Rmin|+Rmax)/Δ.  (8)

It should be noted that the iteration width Δ may be used by the systemadministrator to adjust a granularity (i.e., fineness) with which theoptimum radius value Ro is determined, i.e. the smaller the iterationwidth Δ set by the administrator the higher the number of iterationsdefined by the maximum iteration value kmax and, thus, the finer agranularity of the algorithm for identifying and analyzing traffic flowsof people that attended the one or more public happenings Sn.

In an embodiment of the present invention, since the minimum radiusvalue Rmin is set to −1500 m, the maximum radius value Rmax is set to1500 m and the iteration width Δ is set to 100 m the maximum iterationvalue kmax for the iteration variable k results to be equal to 30 and,therefore, the maximum number of iterations for determining the optimumradius value Ro is limited to 30.

Afterwards, it is checked (step 1234) whether the calculated radiusvalue Rk is lower than, or equal to, the maximum radius value Rmax:Rk≤Rmax.  (9)

In the affirmative case (exit branch Y of decision block 1234), i.e. thecalculated radius value Rk is lower than, or equal to, the maximumradius value Rmax (i.e., Rk≤Rmax) operation returns to step 1206 forstarting a new iteration of the first portion of the algorithm based onthe calculated radius value Rk just increased (at step 1232) by afurther k-th iteration width Δ.

In the negative case (exit branch N of decision block 1234), i.e. thecalculated radius value Rk is greater than the maximum radius value Rmax(i.e., Rk>Rmax), the optimum radius value Ro is computed (step 1236) asthe average of the computed radius values Rk (with 1≤k≤kmax) weighted bythe number DSk of detected public happening Sn within the AoI 107 havingthe AoI radius Ra equal to the same computed radius values Rk, i.e. thedetected number of happening variable DSk, or:

$\begin{matrix}{{Ro} = {\frac{\sum_{k}{{Rk} \cdot {DSk}}}{\sum_{k}{DSk}}.}} & (10)\end{matrix}$

The steps 1206 to 1234 of the first portion of the algorithm foridentifying and analyzing traffic flows of people that attended the oneor more public happenings Sn are iterated until the calculated radiusvalue Rk is greater than the maximum radius value Rmax (i.e., Rk>Rmax),and the optimum radius value Ro is computed (at step 1236).

With the computation of the optimum radius value Ro at step 1236 thefirst portion of the algorithm for identifying and analyzing trafficflows of people that attended the one or more public happenings Sn endsand then a second portion of the algorithm for identifying and analyzingtraffic flows of people that attended the one or more public happeningsSn starts (at step 1238, described in the following). At the end of thefirst portion of the algorithm for identifying and analyzing trafficflows of people that attended the one or more public happenings Sn, theAoI 107 is properly defined by the AoI center C and by the AoI radius Raset equal to the optimum radius value Ro (Ra=Ro).

The second portion of the algorithm for identifying and analyzingtraffic flows of people that attended the one or more public happeningsSn according to an embodiment of the present invention is configured foridentifying the movements and computing O matrices and D matrices (orO-D matrices) related to people that attended one or more publichappenings Sn.

Initially (step 1238) in the system 100, e.g. by a user through the userinterface 125 or by the administrator through the administratorinterface 120, the RoI 500 (with the zones z_(q) by which it isdefined), an origin observation sub-period OsPn for each one of the oneor more public happenings Sn and a destination observation sub-periodDsPn for each one of the one or more public happenings Sn together withthe respective (origin) time slots ts_(m′,n) and (destination) timeslots ts_(m″,n) are defined. Possibly, also the option (described above)for assigning the movements in the sets 1000 and 1100 of O/D matrices700 m′ and 800 m″, respectively, is selected. In other words, for eachone of the one or more public happenings Sn a respective observationtime period TPn may be defined, such observation time period TPncomprises the origin observation sub-period OsPn and the destinationobservation sub-period DsPn mentioned above.

In other embodiment of the invention, the step 1238 just described maybe included in the previously described step 1202, in order to reduce adata entry phase to a single (initial) step.

After the optimum radius value Ro has been computed at step 1236, a setof actually relevant cells 405 a-d is defined (step 1240). This setincludes all the cells 105 b of the mobile communication network 105 forwhich inequality (1) is verified when the AoI radius Ra is set equal tothe optimum radius value Ro, or:Dist(C,B)≤|Rc+Ro|.  (11)

Next (step 1242), the AoI 107 is associated with one (in the consideredexample) of the Q zones z_(q) of the RoI 500, i.e. the happening zonez_(h). Preferably, the happening zone z_(h) is selected as the zonez_(q) of the RoI 500 which comprises the AoI center C. Alternatively,the happening zone z_(h) is the zone z_(q) of the RoI 500 whichcomprises at least a selected portion of the AoI 107 (e.g., a portiongreater than the 50% of the AoI 107 with Ra=Ro).

Then (step 1244), the happening variable n is initialized to unity (n=1)and all the event records er_(v) referred to the observation day gnduring the observation time interval [Ts, Te] and having taken placewithin the actually relevant cells 405 a-d determined at step 1240 areretrieved (step 1246) from the repository 115.

Subsequently (step 1248), a UE list uLn is built. The UE list uLncomprises an identifier of each user corresponding to at least one eventrecord er_(v) among the event records er_(v) referred to relevant cellsthat have been retrieved at previous step 1246.

Once the UE list uLn has been built, a UE variable u is initialized tounity (i.e., u=1) and the O matrices 700 m′,n of the O set 1000 n, i.e.referred to the n-th public happening Sn, and the D matrices 800 m″,n ofthe D set 1100 n, i.e. referred to the n-th public happening Sn, arebuilt with all the respective entries od(i,j) set to zero (step 1250).The UE variable u is used for scanning all the user comprised in the UElist uLn.

All the event records er_(v′) referred to a UE UEu recorded in each oneof the previous days gpn during observation time interval [Tsn, Ten] andhaving taken place within any one of the cells 105 b of the mobilecommunication network 105 are retrieved (step 1252) from the repository115.

Then (step 1254), an average intermediate arrival time iat betweenconsecutive event records er_(v) is computed for the UE UEu. In oneembodiment of the invention, intermediate arrival times for the UE UEuare computed as the difference between time data (i.e., timestamps) oftwo consecutive event records er_(v). Preferably, the averageintermediate arrival time iat is computed on the basis of event recordser_(v) and er_(v′) recorded during the observation day gn retrieved atstep 1246 and the P previous days gpn retrieved at step 1252.

A first event record er_(f) and a last event record er_(I) referred tothe observation day gn during the observation time interval [Tsn, Ten]and having taken place within the actually relevant cells 405 a-ddetermined at step 1240 are identified for the UE UEu (step 1256) and arespective first observation time data tdn_(f) and last observation timedata tdn_(I) are retrieved (step 1258) therefrom.

The first observation time data tdn_(f), the last observation time datatdn_(I) and the average intermediate arrival time iat are combined (step1260) in order to determine a first time fraction f1 that the UE UEu hasspent within the AoI 107 during the observation day gn during theobservation time interval [Tsn, Ten]:

$\begin{matrix}{{f\; 1} = \frac{{{tdn}_{I} - {tdn}_{f} + {iat}}}{{Ten} - {Tsn}}} & (12)\end{matrix}$

Subsequently, a first event record er_(f′) and a last event recorder_(I′) among all the event records er_(v′) referred to the P previousdays gpn during the observation time interval [Tsn, Ten] and havingtaken place within the actually relevant cells 405 a-d determined atstep 1240 are identified for the UE UEu (step 1262) and a respectivefirst previous time data tdpn_(f) and last previous time data tdpn_(I)are retrieved (step 1264) therefrom.

The first previous time data tdpn_(f), the last previous time datatdpn_(I) and the average intermediate arrival time iat are combined(step 1266) in order to determine a second time fraction f2 that the UEUEu has spent within the AoI 107 during the P previous day gpn:

$\begin{matrix}{{{f\; 2} = \frac{{{tdpn}_{I} - {tdpn}_{f} + {iat}}}{Tgpn}},} & (13)\end{matrix}$where Tgpn is total duration of the P previous days gpn, which may becomputed for example in seconds, minutes or hours according to the timeunit (i.e., seconds, minutes or hours) used for time quantities (such asfor example the first previous time data tdpn_(f), the last previoustime data tdpn_(I) and the average intermediate arrival time iat) in thealgorithm for identifying and analyzing traffic flows of people thatattended the one or more public happenings Sn.

Afterwards (step 1268), a person probability pu that the owner of the UEUEu attended at the public happening Sn is computed by combining thefirst time fraction f1 and the second time fraction f2:pu=f1*(1−f2).  (14)

Therefore, the first time fraction f1 and the second time fraction f2may be considered as probabilities. Namely, the first time fraction f1may be construed as the probability that the owner of the UE UEu hasbeen in the AoI 107 during the public happening Sn, while the secondtime fraction f2 may be construed as the probability that the owner ofthe UE UEu has been in the AoI 107 during the previous days gpn.

Next (decision block 1270), the event records er_(v) recorded during theorigin observation sub-period OsPn are considered.

For each UE UEu, it is searched (block 1272) a previous event record er₀directly preceding the first event record er_(f) in the observation daygn and recorded by a cell 105 b of the mobile communication network 105different from the actually relevant cells 405 a-d determined at step1240 (i.e., outside the AoI 107).

If the previous event record er₀ is not found (exit branch N of decisionblock 1272) the algorithm skips to step 1280 described in the following.

Conversely, if the previous event record er₀ is found (exit branch Y ofdecision block 1272) a previous time data tdn₀ and a previous positionpn₀ associated with the previous event record er₀ (step 1274) areretrieved (e.g., from the information contained in the event recorder₀).

Then (decision block 1276), it is searched for a previous zone zn₀,among the zones z_(q) of the RoI 500, that comprises the previousposition pn₀ associated with the previous event record er₀.

If the previous zone zn₀ is not found (exit branch N of decision block1276) the algorithm skips to step 1280 described in the following.

Conversely, if the previous zone zn₀ is found (exit branch Y of decisionblock 1276) a movement between the previous zone zn₀ and the happeningzone zn_(h) is identified (step 1278), provided that the previous zonezn₀ is different from the happening zone zn_(h) (zn₀≠zn_(h)). Themovement is assigned to the entry od(0, h)_(n) of the corresponding Omatrix 700 m′,n or distributed between a plurality of entries od(0,h)_(n) of the corresponding O matrices 700 m′,n referred to time slotsts_(m′,n) comprised in a movement time interval [tdn₀, tdn_(f)]delimited by the previous time data tdn₀ and the first time data tdn_(f)according to the option selected for assigning the identified movementsto the O matrices 700 m′,n.

Preferably, in the embodiment of the present invention, theentry/entries od(0, h)_(n) of the corresponding O matrix/matrices 700m′,n is not simply increased by one unit or by an amount proportional tothe part of each time slot ts_(m′) comprised in the movement timeinterval [tdn₀, tdn_(f)], but it is also combined with, e.g. multipliedby, the person probability pu that the owner of the UE UEu attended atthe public happening Sn computed at step 1268. In other words theentry/entries od(0, h)_(n) of the corresponding O matrix/matrices 700m′,n is the combination, the sum in the considered example, of theperson probability pu that the owner of the UE UEu, for which a movementbetween the previous zone z₀ and the happening zone z_(h) is identified,has also attended the public happening Sn.

In this way, each entries provide a statistical information about as theprobability that the owner of the UE UEu, which generated the consideredprevious event record er₀, has moved into the AoI 107 for attending atthe public happening Sn.

Next (decision block 1280), the event records ery recorded during thedestination observation sub-period DsPn are considered.

For each UE UEu, it is searched (decision block 1282) a next eventrecord er_(I+1) directly following the last event record er_(I) in theobservation day gn and recorded by a cell 105 b of the mobilecommunication network 105 different from the actually relevant cells 405a-d determined at step 1240 (i.e., outside the AoI 107).

If the previous event record er_(I+1) is not found (exit branch N ofdecision block 1282) the algorithm skips to step 1290 described in thefollowing.

Conversely, if the next event record er_(I+1) is found (exit branch Y ofdecision block 1282) a next time data tdn_(I+1) and a next positionpn_(I+1) associated with the next event record er_(I+1) (step 1284) areretrieved (e.g., from the information contained in the event recorder_(I+1)).

Then (decision block 1286), it is searched for a next zone zn_(I+1)among the zones z_(q) of the RoI 500 that comprises the next positionpn_(I+1) associated with the next event record er_(I+1).

If the next zone zn_(I+1) is not found (exit branch N of decision block1276) the algorithm skips to step 1290 described in the following.

Conversely, if the next zone zn_(I+1) is found (exit branch Y ofdecision block 1286) a movement between the happening zone zn_(h) andthe next zone zn_(I+1) is identified (step 1288), provided that the nextzone zn_(I+1) is different from the happening zone zn_(h)(zn_(I+1)≠zn_(h)). The movement is assigned to the entry od(h, I+1)_(n)of the corresponding D matrix 800 m″,n or distributed between aplurality of entries od(h, I+1)_(n) of the corresponding D matrices 800m″,n referred to time slots ts_(m″,n) comprised in a movement timeinterval [tdn_(I), tdn_(I−1)] delimited by the last time data tdn_(I)and the next time data tdn_(I+1) according to the option selected forassigning the identified movements to the D matrices 800 m″,n.

Preferably, in the embodiment of the present invention, theentry/entries od(h, I+1)_(n) of the corresponding D matrix/matrices 800m″,n is not simply increased by unity or by an amount proportional tothe part of each time slot ts_(m″,n) comprised in the movement timeinterval [tdn_(I), tdn_(I+1)], but it is also multiplied by the personprobability pu that the owner of the UE UEu, which generated theconsidered next event record er_(I+1), attended at the public happeningSn computed at step 1268. In this way, each entry provide a statisticalinformation about as the probability that the owner of the UE UEu hasleft the AoI 107 after having attended at the public happening Sn.

Afterwards, the UE variable u is increased by one unit (step 1290; i.e.,u=u+1) and it is checked (step 1292) whether UE variable u is lowerthan, or equal to, a total number of listed UE U (where U is an integernumber) listed in the UE list uLn:u≤U.  (16)

In the affirmative case (exit branch Y of decision block 1292), i.e. theUE variable u is lower than, or equal to, the number U of listed user(u≤U), the operation returns to step 1252 for analyzing the eventrecords er_(v) referred to the next UE UEu.

In the negative case (exit branch N of decision block 1292), i.e. the UEvariable u is greater than the total number U of listed user (u>U), theUE list uLn has been completely scanned. Therefore, the movements andboth the O set 1000 n of O matrices 700 m′,n and the D set 1100 n ofmatrices 800 m″,n referred to the public happening Sn held on theobservation day gn are stored (step 1294) in the repository 115, thenthe day variable n is increased by one unit (step 1296; i.e., n=n+1) andit is checked (step 1298) whether the day variable n is lower than, orequal to, the number N of public happenings Sn (in the same way as doneat previous step 1228):n≤N.  (6)

In the affirmative case (exit branch Y of decision block 1298), i.e. thehappening variable n is lower than, or equal to, the number N of publichappenings Sn (n≤N), the algorithm returns to step 1246 in order toanalyze the next public happening Sn held on the next happening day gn.

In the negative case (exit branch N of decision block 1298), i.e. thehappening variable n is greater than the number N of overall publichappenings Sn (n>N), all the N public happenings Sn have been analyzedand thus the crowd estimation algorithm may be terminated.

Preferably, the algorithm is terminated by providing (step 1300) theresults, i.e. the N O set 1000 n of O matrices 700 m′,n and the N D set1100 n of matrices 800 m″,n, and possibly, the N UE lists uLn and therespective first and second time fractions f1 and f2 for each UE of theUE lists uLn determined at step 1248, to the user through the userterminal 125 for inspection and/or further processing.

The steps 1246 to 1298 of the second portion of the algorithm foridentifying and analyzing traffic flows of people that attended at theone or more public happenings Sn are iterated until all the N publichappenings Sn have been analyzed and thus the algorithm for identifyingand analyzing traffic flows of people that attended at the one or morepublic happenings Sn is terminated (at step 1300) with the provision ofthe results to the user through the user terminal 125.

In summary, the algorithm for identifying and analyzing traffic flows ofpeople that attended the one or more public happenings Sn comprises afirst portion and a second portion.

In its turn, the first portion of the algorithm for identifying andanalyzing traffic flows of people that attended the one or more publichappenings Sn comprises two nested cycles. A first external cycle scans(steps 1206-1234) all the computed radius values Rk between the minimumradius value Rmin and the maximum radius value Rmax, while a firstinternal cycle scans (steps 1210-1228) all the N public happenings Sn tobe analyzed. For each computed radius value Rk respective relevant cells405 a-d and z-score Znk are determined. On the basis of such data (i.e.,respective relevant cells 405 a-d and z-score Znk) the detectedhappening variable DSk is computed and the optimum radius value Ro isidentified. At the end of the first portion of the algorithm foridentifying and analyzing traffic flows of people that attended the oneor more public happenings Sn, the AoI 107 having the optimum radiusvalue Ro is defined.

The second portion of the algorithm comprises two nested cycles as well.A second external cycle scans (steps 1246-1298) all the N publichappening Sn held within the AoI 107, while a second internal cyclescans (steps 1252-1292) all the UE UEu that generated an event recorder_(v) in at least one relevant cell 405 a-d (i.e., the AoI 107) duringthe observation time interval [Tsn, Ten] in the observation day gn. Foreach UE UEu and for each one of the N public happenings Sn, it isdetermined a probability (i.e., the first time fraction f1) that the UEowner has been within one or more of the relevant cells 405 a-bcomprised within the AoI 107 having the AoI radius Ra equal to theoptimum radius value Ro during the observation time interval [Tsn, Ten]on the observation day gn of the public happening Sn and a furtherprobability (i.e., the second time fraction f2) that the UE owner hasbeen within one or more of the relevant cells 405 a-b comprised withinthe same AoI 107 during the P previous days gpn. On the basis of theknowledge of this two probabilities (i.e. time fractions f1 and f2) itis determined the probability that the owner of the UE UEu was a personin the crowd that attended at the public happening Sn. Subsequently, thepositions in the RoI 500 for each UE UEu whose owner was in the crowdthat attended the public happening Sn before and after the observationtime interval [Tsn, Ten] on the observation day gn of the publichappening Sn and outside the AoI 107 are determined and the O set 1000 nof O matrices 700 m′,n and the D set 1100 n of matrices 800 m″,n arecomputed accounting for movements to the AoI 107 and from the AoI 107,respectively.

It should be noted that the algorithm described above may undergoseveral modification, e.g. similar steps with the same functions maysubstitute several steps or portions thereof, some non-essential stepsmay be removed, or additional optional steps may be added, the steps maybe performed in different order, in parallel or overlapped (at least inpart), without departing from the scope of the present invention.

For example in a simplified embodiment of the present invention only asingle set between the O set 1000 n of O matrices 700 m′,n and the D set1100 n of matrices 800 m″,n may be computed in order to determine onlyone between the flow of people that gathered at the AoI 107 or the flowof people leaving the AoI 107.

In another embodiment of the present invention, all the movement of theUE owners that were in the crowd that attended the public happening Snmay be traced within the RoI 500 during the origin observationsub-period OsPn and during the destination observation sub-period DsPn.In this case, event records preceding the previous event record er₀ andfollowing the next event record er_(I+1) will be considered as well.Then also the movements of the UE owners that were in the crowd thatattended at the public happening Sn to the previous zone and from thenext zones from/to others zones z_(q) of the RoI during the originobservation sub-period OsPn and during the destination observationsub-period DsPn, respectively, are identified. This can be achieved bysequences of steps substantially equivalent to steps 1270-1278 and steps1280-1288: for each consecutive event records a movement is identifiedbetween the two zones comprising the position indicated in suchconsecutive event records and corresponding entries of the O/D matricesare increased according to the options selected for assigning values tothe entries of the O/D matrices and according to the person probabilitypu associated with the UE owner of the UE UEu that generated the eventrecords. It should be noted that this leads to compute O matrices and Dmatrices having the same size of the O-D matrices (i.e., having Q rows,and Q columns), and providing information on the flows of people thatattended the one or more shows Sn within the whole RoI 500.

The system 100 for identifying and analyzing traffic flows of peoplethat attended the one or more public happenings Sn according to anembodiment of the present invention allows a posteriori estimation ofmovements (i.e., traffic flows) of people that attended one or morepublic happenings Sn in a reliable way and allows properly identifying(by determining the optimum radius value Ro) an effective exTension ofthe AoI 107 associated with each of the one or more public happeningsSn.

The invention claimed is:
 1. A method of estimating flows of personsthat gathered at an Area of Interest for attending a public happeningduring a time interval on a day, wherein said Area of Interest isdefined by an Area of Interest center and an Area of Interest radius andis covered by a mobile telecommunication network having a plurality ofcommunication stations each of which is adapted to manage communicationsof user equipment in one or more served areas in which the mobiletelecommunication network is subdivided, the method comprising the stepsof: a) defining a plurality of calculated radius values of the Area ofInterest radius, and, for each calculated radius value: b) identifying afirst number of user equipment associated with at least one event recordof a corresponding event of interaction occurred between the userequipment and the mobile communication network during the time intervalon the day within the Area of Interest; c) identifying a second numberof user equipment associated with at least one event record of acorresponding event of interaction occurred between the user equipmentand the mobile communication network during the time interval for eachday of a predetermined number of previous days preceding the day withinthe Area of Interest; d) combining the first number of user equipmentand the second numbers of user equipment for obtaining a statisticalquantity; e) detecting the occurrence of the public happening if thestatistical quantity reaches a certain threshold; f) computing anoptimum radius value of the Area of Interest radius as the average ofthe calculated radius values within which the public happening isdetected; g) identifying persons that gathered for attending at thepublic happening within an Area of Interest having the Area of Interestradius equal to the optimum radius values during the time interval onthe day within the Area of Interest based on a first time fractionindicating a probability that the user equipment has been in the Area ofInterest during the time interval on the day and on a second timefraction indicating that the user equipment has been in the Area ofInterest during the previous days for each user equipment identified atstep b); h) computing at least one matrix accounting for movements ofpersons identified at step g) within a Region of Interest comprising theArea of Interest to the Area of Interest during at least one observationtime period comprising the time interval, and i) computing at least onematrix accounting for movements of persons identified at step g) withina Region of Interest comprising the Area of Interest from the Area ofInterest during at least one observation time period comprising the timeinterval.
 2. The method according to claim 1, wherein the publichappening comprises a plurality of public happenings, the method furthercomprising the step of: j) iterating steps b) to e) for each one of thepublic happenings of the plurality of public happenings, and wherein thestep f) of computing an optimum radius value of the Area of Interestradius as the average of the computed radius values within which thepublic happening is detected, comprises: computing an optimum radiusvalue of the Area of Interest radius as the average of the computedradius values weighted by a number of detected public happenings withinthe Area of Interest having the Area of Interest radius equal to thesame computed radius values, said number of detected public happeningsbeing the sum of the public happenings determined by iterating step e).3. The method according to claim 2, wherein the steps h) of computing atleast one matrix accounting for movements of persons identified at stepg) within a Region of Interest comprising the Area of Interest to theArea of Interest during at least one observation time period comprisingthe time interval, and i) of computing at least one matrix accountingfor movements of persons identified at step g) within a Region ofInterest comprising the Area of Interest from the Area of Interestduring at least one observation time period comprising the timeinterval, comprise: k) subdividing the Region of Interest into at leasttwo zones; l) associating a first zone of the at least two zones withthe Area of Interest, the first zone comprising at least partially theArea of Interest; m) subdividing the at least one time period into oneor more time slots; n) identifying a number of persons that moved from asecond zone of the at least two zones to the first zone comprising atleast partially the Area of Interest during each one of the one or moretime slots of the at least one observation time period, and o)identifying a number of persons that moved to the second zone of the atleast two zones from the first zone comprising at least partially theArea of Interest during each one of the one or more time slots of the atleast one observation time period.
 4. The method according to claim 3,wherein the step n) of identifying a number of persons that moved from asecond zone of the at least two zones to the first zone comprising atleast partially the Area of Interest during each one of the one or moretime slots of the at least one observation time period, comprises:identifying as the second zone the zone of the Region of Interest thatcomprises a previous position associated with a previous event record ofa corresponding previous event of interaction occurred between the userequipment and the mobile communication network during the observationtime period on the day within the Region of interest, the previous eventrecord being recorded before a first event record of a correspondingevent of interaction occurred between the user equipment and the mobilecommunication network during the time interval on the day within theArea of Interest.
 5. The method according to claim 4, wherein the stepn) of identifying a number of persons that moved from a second zone ofthe at least two zones to the first zone comprising at least partiallythe Area of Interest during each one of the one or more time slots ofthe at least one observation time period, further comprises: p)computing an origin matrix for each one of the one or more time slots inwhich the at least one time period has been subdivided, each entry ofthe origin matrix being indicative of the number of persons that, duringthe corresponding time slot, moved to the first zone comprising at leastpartially the Area of Interest from the second zone of the at least twozones.
 6. The method according to claim 5, wherein the step p) ofcomputing an origin matrix for each one of the one or more time slots inwhich the at least one time period has been subdivided, each entry ofthe origin matrix being indicative of the number of persons that, duringthe corresponding time slot, moved to the first zone comprising at leastpartially the Area of Interest from the second zone of the at least twozones, comprises: increasing a value of the entry indicative of personsthat moved from the second zone of the at least two zones to the firstzone comprising at least partially the Area of Interest of the originmatrix associated with a time slot comprising a previous time dataassociated with the previous event record.
 7. The method according toclaim 5, wherein the step p) of computing an origin matrix for each oneof the one or more time slots in which the at least one time period hasbeen subdivided, each entry of the origin matrix being indicative of thenumber of persons that, during the corresponding time slot, moved to thefirst zone comprising at least partially the Area of Interest from thesecond zone of the at least two zones, comprises: increasing a value ofthe entry indicative of persons that moved from the second zone of theat least two zones to the first zone comprising at least partially theArea of Interest of the origin matrix associated with a time slotcomprising a first time data associated with the first event record. 8.The method according to claim 5, wherein the step p) of computing anorigin matrix for each one of the one or more time slots in which the atleast one time period has been subdivided, each entry of the originmatrix being indicative of the number of persons that, during thecorresponding time slot, moved to the first zone comprising at leastpartially the Area of Interest from the second zone of the at least twozones, comprises: identifying an origin movement time interval delimitedby a previous time data associated with the previous event record of thecorresponding previous event and a first time data associated with thefirst event record of the corresponding first event, and increasing avalue of the entry indicative of persons that moved from the second zoneof the at least two zones to the first zone comprising at leastpartially the Area of Interest of the origin matrices associated withtime slots comprised at least partially in the movement time interval.9. The method according to claim 5, wherein the step o) of identifying anumber of persons that moved to the second zone of the at least twozones from the first zone comprising at least partially the Area ofInterest during each one of the one or more time slots of the at leastone observation time period, comprises: identifying as the second zonethe zone of the Region of Interest that comprises a next positionassociated with a next event record of a corresponding next event ofinteraction occurred between the user equipment and the mobilecommunication network during the observation time period on the daywithin the Region of interest, the next event record being recordedafter a last event record of a corresponding last event of interactionoccurred between the user equipment and the mobile communication networkduring the time interval on the day within the Area of Interest.
 10. Themethod according to claim 9, wherein the step o) of identifying a numberof persons that moved to the second zone of the at least two zones fromthe first zone comprising at least partially the Area of Interest duringeach one of the one or more time slots of the at least one observationtime period, further comprises: q) computing a destination matrix foreach one of the one or more time slots in which the at least one timeperiod has been subdivided, each entry of the destination matrix beingindicative of the number of persons that, during the corresponding timeslot, moved from the first zone comprising at least partially the Areaof Interest to the second zone of the at least two zones.
 11. The methodaccording to claim 10, wherein the step q) of computing a destinationmatrix for each one of the one or more time slots in which the at leastone time period has been subdivided, each entry of the destinationmatrix being indicative of the number of persons that, during thecorresponding time slot, moved from the first zone comprising at leastpartially the Area of Interest to the second zone of the at least twozones, comprises: increasing a value of the entry indicative of personsthat moved to a second zone of the at least two zones from the firstzone comprising at least partially the Area of Interest of thedestination matrix associated with a time slot comprising a last timedata associated with the last event record.
 12. The method according toclaim 10, wherein the step q) of computing a destination matrix for eachone of the one or more time slots in which the at least one time periodhas been subdivided, each entry of the destination matrix beingindicative of the number of persons that, during the corresponding timeslot, moved from the first zone comprising at least partially the Areaof Interest to the second zone of the at least two zones, comprises:increasing a value of the entry indicative of persons that moved to asecond zone of the at least two zones from the first zone comprising atleast partially the Area of Interest of the destination matrixassociated with a time slot comprising a next time data associated withthe next event record.
 13. The method according to claim 10, wherein thestep q) of computing a destination matrix for each one of the one ormore time slots in which the at least one time period has beensubdivided, each entry of the destination matrix being indicative of thenumber of persons that, during the corresponding time slot, moved fromthe first zone comprising at least partially the Area of Interest to thesecond zone of the at least two zones, comprises: identifying adestination movement time interval delimited by a last time dataassociated with the last event record of the corresponding last eventand a next time data associated with the next event record of thecorresponding next event, and increasing a value of the entry indicativeof persons that moved to a second zone of the at least two zones fromthe first zone comprising at least partially the Area of Interest of thedestination matrices associated with time slots at least partiallycomprised in the destination movement time interval.
 14. The methodaccording to claim 3, wherein the step g) of identifying persons thatgathered for attending at the public happening within an Area ofInterest having the Area of Interest radius equal to the optimum radiusvalues during the time interval on the day within the Area of Interestbased on a first time fraction indicating a probability that the userequipment has been in the Area of Interest during the time interval onthe day and on a second time fraction indicating a probability that theuser equipment has been in the Area of Interest during the previous daysfor each user equipment identified at step b), comprises estimating aprobability that each person is attending at the public happening, foreach person associated with a respective user equipment of the firstnumber identified at step c), based on the first time fraction and thesecond time fraction.
 15. The method according to claim 14, wherein thestep n) of identifying a number of persons that moved from a second zoneof the at least two zones to the first zone comprising at leastpartially the Area of Interest during each one of the one or more timeslots of the at least one observation time period, further comprises:computing the number as a combination of the probabilities of eachpersons that moved from a second zone of the at least two zones to thefirst zone comprising at least partially the Area of Interest duringeach one of the one or more time slots of the at least one observationtime period is attending at the public happening.
 16. The methodaccording to claim 15, wherein the step o) of identifying a number ofpersons that moved to the second zone of the at least two zones from thefirst zone comprising at least partially the Area of Interest duringeach one of the one or more time slots of the at least one observationtime period, further comprises: computing the number as a combination ofthe probabilities of each persons that moved to the second zone of theat least two zones from the first zone comprising at least partially theArea of Interest during each one of the one or more time slots of the atleast one observation time period is attending at the public happening.17. The method according to claim 3, wherein the step k) of subdividingthe Region of Interest into at least two zones, comprises: subdividingthe Region of Interest into a plurality of zones, and wherein the stepn) of identifying a number of persons that moved from a second zone ofthe at least two zones to the first zone comprising at least partiallythe Area of Interest during each one of the one or more time slots ofthe at least one observation time period, further comprises: identifyinga number of persons that moved from each zone of the plurality of zonesto the first zone comprising at least partially the Area of Interestduring each one of the one or more time slots of the at least oneobservation time period, and wherein the step o) of identifying a numberof persons that moved to the second zone of the at least two zones fromthe first zone comprising at least partially the Area of Interest duringeach one of the one or more time slots of the at least one observationtime period, comprises: identifying a number of persons that moved toeach zone of the plurality of zones from the first zone comprising atleast partially the Area of Interest during each one of the one or moretime slots of the at least one observation time period.
 18. The methodaccording to claim 10, further comprising the step of: r) iteratingsteps l) to q) for each one of the public happenings.
 19. A systemcoupled with a wireless telecommunication network for estimating flowsof persons that gathered at an Area of Interest, the system comprising:a computation engine adapted to process data retrieved from a mobiletelephony network; a repository adapted to store data regardinginteractions between the user equipment and the mobile telephonynetwork, computation results generated by the computation engine and,possibly, any processing data generated by and/or provided to thesystem, and an administrator interface operable for modifying parametersand/or algorithms used by the computation engine and/or accessing datastored in the repository characterized by further comprising a memoryelement storing a software program product configured for implementingthe method of claim 1 through the system.
 20. The system according toclaim 19, further comprising at least one user interface adapted toreceive inputs from, and to provide output to a user of the system, theuser comprising one or more human beings and/or one or more externalcomputing systems subscriber of the services provided by the system.